System and method for extraction and cryopreservation of bone marrow

ABSTRACT

Methods are provided for extracting bone marrow cells from bone obtained from deceased donors, for preparing the bone marrow for cryopreservation and for obtaining desired cells from cryopreserved and fresh bone marrow.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to co-pending U.S. Utility applicationSer. No. 16/734,713, filed on Jan. 6, 2020, to U.S. ProvisionalApplication No. 62/834,087, filed on Apr. 15, 2019, and entitled “Systemand Method for Collecting and Preserving Bone Marrow for Clinical Use”and to co-pending U.S. Provisional Application No. 62/938,480, filed onNov. 21, 2019, and entitled “System and Method for Extraction andCryopreservation of Bone Marrow”. The entire disclosures of all threeapplications are expressly incorporated herein by reference.

BACKGROUND

Bone marrow for clinical purposes is currently harvested from HLAmatched siblings or optimally matched unrelated donors. Other graftsources are also now utilized including mismatched haploidenticalrelated or unrelated donors and umbilical cord blood (CB). Whentransplanted into patients with certain diseases, the hematopoietic stemcells (HSCs) in the donor bone marrow engraft in the patient andreconstitute immune and hematopoietic systems.

Bone marrow is also a good source for mesenchymal stromal/stem cells(MSCs) which are self-renewing, multipotent progenitor cells withmultilineage potential to differentiate into cell types of mesodermalorigin, such as adipocytes, osteocytes, and chondrocytes. In addition,MSCs can migrate to sites of inflammation and exert potentimmunosuppressive and anti-inflammatory effects through interactionsbetween lymphocytes associated with both the innate and adaptive immunesystem.

Currently bone marrow is typically collected through a hole created inthe cortical bone with a trocar needle and then using a bone marrowaspiration needle and a syringe to draw the marrow into the syringe.Multiple syringes are usually necessary to extract all of the marrowfrom the bone. The syringes are then removed from the sterile field andeach syringe is connected to a collection bag containing anticoagulantsand the marrow is pushed into the bag. This step is repeated many times,typically in both pelvic bones, and can result in contamination of theaspirate.

It was recognized sixty years ago that banked whole bone marrow (BM)from deceased donors are also a very viable source of HSCs. Recovery ofhighly functional BM from deceased organ donors is conceptually similarto procurement of organs and tissues that has occurred for decades,leading to more than 30,000 organ transplants and 1 million tissuetransplants performed each year in the US alone. Bone marrow HSC arehardier than sensitive organs and most tissues, as these cells naturallyhave evolved to reside in a hypoxic environment within the BM niche and,thus, are able to withstand prolonged periods of ischemia. HSCs aretypically in a quiescent (GO) state, and therefore require littlemetabolic substrates and produce little waste. The CD34+ HSC andprogenitors within deceased organ donor BM have been found to be highlyviable. Published values for viability of CD34+ cells isolated fromorgan donor BM (even with non-optimized and non-validated recovery andprocessing procedures) was 95.2%, compared to 93.5% for living donor BM.Deceased organ donors are a rich source of viable BM cells and arestatistically indistinguishable from living donors by CD34+ viabilityand total nucleated cells (TNC). Higher yields of CD34+ HSC and largerquantities of BM from organ donors allows banking of multiple BM units(>2 units at ˜2×10⁶ CD34+ cells/kg, based on a 70 kg patient) fortransplanting to multiple recipients as well as enabling certainty ofbeing able to re-transplant in cases of primary graft failure.

Nevertheless, multiple barriers have prevented mainstream use ofcadaveric bone marrow. One significant barrier has been in finding astreamlined process for controlled extraction and preservation ofdeceased donor bone marrow and the cell yields from that bone marrow.Current best-practice BM recovery from cadaveric organ donors involvesmultiple manual steps requiring several skilled operators. Typically,vertebral bodies (VB) are recovered by the transplant surgeon andinitially cleaned in the OR prior to transport to the processing lab,where they are cleaned again very carefully to remove all remnants oftough connective tissue prior to further processing steps. Next the VBsare processed in groups of 3 by first manually cutting the bone intocubes and then feeding the cubes into a bone grinding system. The groundbone is then tumbled and rinsed multiple times, and cells are finallyconcentrated through centrifugation. Because no more than 3 VBs can beprocessed at one time, this procedure must be repeated three times perdonor. This entirely manual current process typically requires 40 hoursof total labor with almost 11 hours of processing time, at a typicalcost of over $10,000 per donor.

Another concern regarding the use of cadaveric bone relates to thecryopreservation, banking and recovery of the bone. In particular, theconcern relates to the quality of viable cells, such as HSCs, which canbe obtained from donor bone, particularly for bones recovered atgeographically dispersed locations and shipped long distances to acryo-banking facility. Every step of the process for recovering bonefrom a deceased donor involves ischemia, or a shortage of oxygen to thecells in the bone marrow. It is known that variations in warm and coldischemia time can influence the quality of HSCs and progenitor cellsderived from cadaveric bone. Current tissue-banking guidelines in the USallow tissues to be recovered from deceased donors up to 24 hoursfollowing asystole, provided the body is refrigerated within 12 hours ofcardiac arrest. However, body cooling is a variable that has not beeninvestigated systematically in relation to the recovery of bone marrow.There is a need for a method for determining tolerance limits for bothwarm and cold ischemia which, if exceeded, would likely render thequality and functionality of recovered cells unacceptable fortherapeutic use.

SUMMARY OF THE INVENTION

The systems and methods disclosed herein provide a needed complement toexisting bone marrow and stem cell sources. Typically, less thanone-half of the patients waiting for an allo-BM transplant receive thetransplant. The living donor BM registry, BM cryopreservation andautotransplantation, and umbilical cord blood banking have providedlifesaving solutions for thousands of patients with hematologicdiseases; however these methods still suffer from severe limitationstied to supply and logistics and would benefit from this valuablecomplement. Additionally, though rare, adverse events are possible fromliving bone marrow donation (i.e. the risk of death associated with bonemarrow donation is 1:10,000)), and while peripheral blood stem celldonation is currently much more utilized, nearly all of those donorswill experience bone pain, 1 in 4 will have significant headache,nausea, or citrate toxicity, and 1/5,000 will experience splenic ruptureor other fatal complication. Additionally, the long term effects of stemcell mobilizing agents are not yet known. The technical feasibility ofcadaveric BM banking and donation has been demonstrated in principle,yet these vast alternative supplies are currently discarded due toissues directly addressed by this invention.

Banking BM as disclosed herein provides a ready mechanism to match manypatients who cannot find a living donor. It can greatly increasepost-transplant survival rates for many patients with rapidlyprogressing diseases and poor prognosis by allowing on-demandtransplantation and reducing waiting times for these patients from manymonths to only 1-2 days. And importantly, this approach provides largequantities of BM from each donor, sufficient to allow engraftment ofhematopoietic stem and progenitor cells (HSPCs) for several patients andenabling immediate repeat BM transplantation when needed.

The methods and systems disclosed herein enable large supplies ofon-demand bone marrow for national emergency preparedness efforts. Theurgent unmet need for on-demand bone marrow and stem cell transplants asa medical countermeasure for nuclear accidents or attacks has been welldocumented by HHS, BARDA's multi-billion dollar Project Bioshield, andthe Dept. of Defense. The present disclosure also provides needed bonemarrow for emerging applications such as immune tolerance induction andbeyond. A protocol for processing and the actual banking of BM fromorgan donors for extended periods of time is critical to this approach.Additionally, patients who receive deceased donor organ transplantstoday could benefit from this therapy when it becomes available in thefuture, if BM from these donors is banked—making this method immediatelybeneficial to vital organ transplant recipients. If successful, otherpromising methods and treatments being researched have the potential togreatly enhance the value of cadaveric BM procurement and banking usingthe proposed method, including HLA Mismatched Unrelated Donor (mMUD) BMtransplantation—making large supplies of banked bone marrow immediatelyusable for most recipients who need a BM transplant quickly,particularly to address severe forms of autoimmune disorders, geneticdiseases, Multiple Scleroris, and Type 1 Diabetes.

In one aspect, a method is provided for obtaining bone marrow cells fromdeceased donor bone that comprises the steps of: obtaining a bone from adeceased donor; cleaning the bone of soft tissue; grinding the bone intobone pieces; filtering and rinsing the ground bone to produce a liquidcomposition; centrifuging the liquid composition of the filtered andrinsed ground bone to concentrate bone marrow cells; and extracting thebone marrow cells into a sterile container for cryopreservation andsubsequent isolation of target cells.

In a further aspect, a bone cutting tool is provided for use inpreparing the bone for grinding in the method described above. The bonecutting tool comprises two handles, a knife element and a ratchet andpawl mechanism for driving the knife element into the bone in which thecomponents are connected to each other by elongated pins passing throughopenings in the respective components. The pins are removably retainedby at least one removable retaining ring such that the bone cutting toolcan be readily disassembled for cleaning and re-assembled aftercleaning. The bone cutting tool is formed of medical grade stainlesssteel with the surfaces passivated to endure the sterilizationenvironment.

In another aspect, a method is provided for recovering cells fromdeceased donor bone marrow that comprises the steps of: obtaining bonefrom a deceased donor; processing the bone to extract bone marrow cellsfrom the bone; obtaining a reduced density FICOLL® solution having adensity of 1.063-1.052 gm/mL; introducing the reduced density FICOLL®solution into a centrifuge tube to form a FICOLL® gradient; layering theextracted bone marrow cells over the FICOLL® gradient in the centrifugetube; centrifuging the tubes containing the FICOLL® gradient and bonemarrow cells; harvesting the buffy coat cells from within the centrifugetubes; and washing the harvested cells for subsequent use or processing.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1D are views of a hand-operated bone cutting tool according toone aspect of the present disclosure.

FIG. 2 is a view of a filtration system according to one feature of thepresent disclosure.

FIG. 3 is a view of a sterile bag containing a bone marrow pelletprocessed according to the methods of the present disclosure.

FIG. 4 is a view of the sterile bag of FIG. 3 with a clip engaging thebag to separate the fat from the bone marrow pellet.

FIG. 5 shows the set-up for isolation of the bone marrow pellet

FIG. 6 is a perspective view of a cooling box according to one aspect ofthe present disclosure.

FIG. 7 is a flowchart of the steps of one method according to thepresent disclosure FIGS. 8A and 8B are side and perspective views of anautomated bone processing system according to one aspect of the presentdisclosure.

FIGS. 9A and 9B are perspective views of a bone debriding station of thesystem shown in FIGS. 78A-8B.

FIGS. 10A and 10B are perspective and front views of a bone grindingstation of the system shown in FIGS. 8A, 8B.

FIG. 11 is a perspective view of a sieve station of the system shown inFIGS. 8A, 8B.

FIGS. 12A-12C are tables of CD34+ cell viability as a function of warmand cold ischemia times, without and without body cooling.

FIGS. 13A-13C are tables of CFU-Total as a function of warm and coldischemia times, with and without body cooling.

FIGS. 14A-14C are tables of CFU-Total as a function of warm and coldischemia times, with and without body cooling.

FIG. 15 is a chart of viability threshold as a function of warm ischemiatimes and cold ischemia times.

FIG. 16A to FIG. 16D show processing of a typical vertebral column toisolate vBA-MSC.

FIG. 17A and FIG. 17E show surface antigen phenotype and trilineagedifferentiation of vBA-MSC.

FIG. 18 shows colony forming unit-fibroblast (CFU-F) potential ofvBA-MSCs.

FIG. 19 shows vBA-MSC suppression of T cell activation.

FIG. 20 shows trilineage differentiation of vBA-MSC and MSC isolatedfrom deceased donor vertebral body BM and BM aspirated from the iliaccrests of living donors.

FIG. 21A shows the observed and potential cumulative growth yields ateach passage of vBA-MSC from 3 donors and FIG. 21B shows the cumulativevBA-MSC population doublings at passages 0-9.

FIG. 22 shows the cumulative ischemia times for all donors used in thestudy of Example 2 for which complete data were available. Donors areranked from shortest to longest total ischemia times which is acomposite of WIT, CIT and BCT.

FIG. 23 is a table showing a comparison of (a) Processing Facility, (b)Bone Type, and (c) Body Cooling.

FIG. 24 is a table showing a % CD34+Beta Regression Model. The modelshows the effects of warm ischemia, body cooling and cold ischemia, onthe percentage of viable CD34+ cells, controlling for the influence ofother covariates.

FIG. 25 is a table showing CFU-Total Linear Regression: Effects of warmischemia, body cooling and cold ischemia on number of CFUs/10⁵ TNCcontrolling for the influences of facility, experience (number of casesprocessed) and bone type.

FIG. 26 is a table showing CFU-GM Linear Regression: Effects of warmischemia, body cooling and cold ischemia on number of CFUs/10⁵ TNCcontrolling for the influence of bone type.

FIG. 27 is a table showing % CD34+Beta Regression: Effects of warmischemia, body cooling and cold ischemia on number of viable CD34+ cellsas a percentage of total CD34+.

FIG. 28 is a table showing CFU-Total Linear Regression: Effects of warmischemia, body cooling and cold ischemia on number of CFUs per 10⁵cells.

FIG. 29 is a table showing CFU-GM Linear Regression: Effects of warmischemia, body cooling and cold ischemia on number of CFUs per 10⁵ GMcells.

DETAILED DESCRIPTION

For the purposes of promoting an understanding of the principles of thedisclosure, reference will now be made to the embodiments illustrated inthe drawings and described in the following written specification. It isunderstood that no limitation to the scope of the disclosure is therebyintended. It is further understood that the present disclosure includesany alterations and modifications to the illustrated embodiments andincludes further applications of the principles disclosed herein aswould normally occur to one skilled in the art to which this disclosurepertains.

The present disclosure provides a clinically oriented research protocoland system that is modified to be implemented in an industrial contextwithin state-of-the-art clean rooms. One aspect of the disclosed systeminvolves, among other things, debridement of the incoming donor bone,initial fragmentation using a custom-made surgical stainless steelcutter, and grinding of the fragmented bone to ˜3 mm fragments sizes.These refinements provide a system in which skilled tissue processingtechnicians can process sets of donor bones within a 6-hour window toyield meaningful quantities of viable marrow.

A first step in the process described herein is the evaluation ofpotential sources of deceased donor bone marrow. In processing longbones from a donor, such as the tibia, it has been found that due toconversion of red marrow to yellow with age, red marrow is limited tothe ends of the long bones and varies dramatically from donor to donor.It has also been determined that mixed yellow-red marrow is poorquality, compared to wholly red marrow, such as marrow from thevertebral bodies or the ilium, and mixed yellow-red marrow containsfatty infiltrate that complicates subsequent processing steps. The bestdonor long bone in certain clinical experiments yielded only 1/100^(th)BM cells/kg compared to cells obtained from the ilia of the same donor.It has been determined, then, that long bone processing is preferablyonly performed in special cases, such as involving extra valuable“universal” HLA types or bone marrow with the HIV resistant delta 32mutation.

In contrast, the vertebral body and the ilium represent the largestconsistent reservoirs of high quality red marrow. Utilizing both sourceshas optimized the recovery of bone marrow, particularly with theimplementation of the industrialized, scalable, GMP process disclosedherein. The completion of the process disclosed herein results incryopreservation of a final product configuration of storing a 60-70 mlvolume at a target of 100-150 million TNC/ml in standard blood bags,similar to the product configuration already used for cryopreserved BMfor autologous transplants.

Preparing the Donor Bone

For the purposes of illustration, the donor bone is assumed to bevertebral bodies. However, it is understood that the methods describedherein can be used on the ilium, a combination of the vertebral bodiesand ilium, or other bones suitable for extraction of bone marrow andcells from the marrow, even donor bones with lower expected yields.

It is understood that the donor bones can be procured according to fixedprotocols for clinical recovery. Bones can be recovered by surgeons orby personnel at a trained OPO (organ procurement organization) using anosteotome and mallet from consented organ and tissue donors. Unprocessedbones are preferably wrapped in sponges and towels soaked in saline toensure moisture retention during hypothermic shipment on wet ice at atemperature of 0 to 10° F. to a processing facility.

The process for preparing the donor bone can occur soon after the boneis obtained from the deceased donor or can occur after the donor bonehas been shipped in a hypothermic environment to a processing facility.Since the donor bone can experience prolonged periods of ischemia duringrecovery and shipment to the processing facility, care must be taken totrack the length and type of ischemia—i.e., warm ischemia and coldischemia. As described in more detail herein, bone subject topredetermined periods of warm and/or cold ischemia are suitable forobtaining meaningful quantities of viable bone marrow cells.

In the first step of processing the donor bone, the bone is debrided inan ISO-5 (class 100) environment (biosafety cabinet) with an ISO-7(class 10,000) background (clean room), with special care taken tosterilize the bag containing the donor bone, such as by spraying with70% isopropanol. In one embodiment, the debridement is conductedmanually using scalpels, osteotomes and gouges. In processing vertebrae,typically a spinal segment including multiple vertebral levels will beprovided. In a typical case, the spine segment runs from T8 to L5, forten vertebral bodies. During initial debridement of the spinal segment,when enough soft tissue has been removed to visualize the pedicles, thepedicles are removed using either a tissue processing band saw or a bonesaw, such as the Stryker System 6 Saw (Stryker, Kalamazoo, Mich.).Special care is taken to avoid breaching the cortical bone which wouldexpose the cancellous bone, to ensure that the hypoxic cancellous bonemarrow remains protected throughout the entire debriding process. Theanterior element of the vertebral bodies remain, while the pedicles andposterior elements are discarded.

Using a boning knife or tissue processing band saw, the vertebral bodiesare separated at the intervertebral discs. The intervertebral disc andsoft tissue remaining on each vertebral body is removed with a scalpel,scissors and/or osteotomes, leaving clean, separated VBs. In the case ofdonor ilium, the soft tissue can be removed with gouges and a scalpel,with special care again taken to ensure that the cortical bone is notbreached. Any anatomical pathologies or injuries of the bone are notedand recorded as part of the batch record for the marrow ultimatelyobtained from the bones. Bones damaged during the recovery process arediscarded.

The VBs are placed into a sterile bag and submerged in a 10% bleachsolution, yielding a concentration of 5,000 ppm free chlorine, for apredetermined period, typically 10-25 minutes. Bleach has a broadspectrum of anti-microbial activity, does not leave a toxic residue, isunaffected by water hardness and is fast acting. At the end of theperiod, the bones are transferred to another sterile bag and submergedin a 3% hydrogen peroxide (H₂O₂) solution. The bag is closed and shakenbriefly to ensure that the entire surface of the bone is in contact withthe solution. Most living cells include catalase, which is an enzymethat catalyzes the breakdown of H₂O₂ into H₂O and O₂. This breakdownmanifests as foam or froth when the H₂O₂ solution contacts soft tissuebut not bone. The foam level can be observed as an indication of theamount of soft tissue remaining on the bone. This observation can beperformed manually by a human processor or, in another embodiment, by anautomated processor. The automated processor incorporates avisualization device, such as a camera, and object recognition softwarethat can determine foam levels within the bag. The addition of an inertcontrast dye can help the human or automated processor detect the foamlevel. If any foam or froth is observed, the bone is returned forfurther processing to remove all of the remaining soft tissue from thebone. Once the VBs or ilium has been cleaned of all soft tissue, thebones are transferred to a new sterile bag. The bag is filled with 1 Lof PLASMA-LYTE™ (multiple electrolytes injection obtained from BaxterHealthcare, Ltd.), or other suitable sterile, nonpyrogenic isotonicsolution. The bag is closed and shaken briefly to ensure that the entirebone is contacted with the PLASMA-LYTE™.

Extracting the Bone Marrow

The bone is removed from the bag and from the PLASMA-LYTE™, and asterile gauze or sponge is used to absorb any liquid remaining on theVBs. In one approach, a saw and/or anvil shears are used to cut the VBsare cut into smaller pieces, such as 1.5 cm² pieces, that are smallenough for fragmenting with a bone grinder. In order to simplify theprocess and for increased safety to the processing personnel, a custombone cutting tool 100 is provided as illustrated in FIGS. 1A-1D that isused to cut the VBs into the smaller pieces. The bone cutting tool 100includes a knife element 102 with a knife edge 102 a configured topenetrate and sever bone. The knife element 102 is pivotably connectedat a pivot 105 to a fixed handle component 104. The fixed handlecomponent 104 includes a jaw end 104 a that is juxtaposed with the knifeedge 102 a to cut through a bone retained in the jaw end. As shown inFIG. 1B, the fixed handle component includes two plates 104 d that arespaced apart to receive the knife component therebetween, as best seenin FIGS. 1B-1D. In particular, the knife edge 102 a passes between thetwo plates 104 d at the jaw end 104 a, which ensures that the knife edge102 a passes through the bone captured by the jaw end 104 a. The jaw end104 a can include two recesses 104 c separated by a ridge 104 b thatengages the bone and helps hold the bone in the jaw end as the knifeedge 102 a passes through the bone. Alternatively, a single recess canbe defined in the jaw end configured to retain the bone. The pivot 105is in the form of a pin that extends through the two plates 104 d andthe knife component 102 sandwiched between the plates.

The bone cutting tool 100 includes a lever handle 107 that is pivotablymounted to the fixed handle 104 at a pivot 109. The pivot can include abiasing element, such as a torsion spring (not shown) configured to biasthe lever handle 107 away from the fixed handle 104. The lever handle isthus configured to be pivoted toward the fixed handle when the twohandles are grasped and squeezed by the user, and then to pivot awayfrom the fixed handle when the user releases the grip on the handles. Itcan be appreciated that the lever handle 107 is formed of two plates 107a with the fixed handle 104 sandwiched between the two plates 107 a atthe pivot 109. As with the pivot 105 the pivot 109 is in the form of apin that extends through the two plates 107 a and through the fixedhandle 104. Both handles 104, 107 include respective gripping plates106, 108 that are contoured to be grasped by the palm and fingers of theuser. The gripping plates 106, 108 connected the pairs of plates 104 d,107 a that form the two handles. The surfaces of the gripping plates caninclude a non-slip feature to facilitate grasping the tool.

The lever handle 107 includes a pawl 112 that is pivotably mounted atpivot 113 to the lever handle. As with the other pivots, the pivot 113is a pin that extends through the pair of plates 107 a that form thelever handle 107 and through an end of the pawl 112. The pivot 113includes a biasing element, such as a torsion spring (not shown), thatbiases the pawl 112 toward a ratchet component 110 of the knife element102. The end of the pawl 112 is configured to engage teeth 110 a on theratchet component 110 to rotate the ratchet component, and thus theknife element 102, in a counter-clockwise direction as viewed in FIG.1A. In particular, as the user squeezes the two handles together, thelever handle 107 moves toward the fixed handle 104 which pushes the pawl112 upward against a tooth 110 a of the ratchet to pivot the ratchetupward and counter-clockwise. When the user releases the lever handle,the handle moves away from the fixed handle, causing the pawl 112 toslide down the ratchet in the clockwise direction until it reachesanother tooth 110 a. Repeated squeezing of the two handles thus casesthe pawl to successively rotate the ratchet. The knife element 102 alsoincludes an integral link 103 that is pivotably connected to a free link114 at a pivot 116. The free link 114 is pivotably connected to thelever handle 107 at a pivot 115. The integral link 103 and free link 114hold the knife element 102 in position as the pawl traverses the ratchetcomponent 110. The pivot 115 of the free link is a pin, like the otherpivots, and includes a biasing element, such as a torsion spring (notshown) that biases the lever handle 107 away from the fixed handle 104.This allows the user to close and release the handles of the tool tosuccessively advance the pawl 112 along the ratchet component 110, whichsuccessively advances the knife edge 102 a into the bone.

In one feature of the bone cutting tool 100 of the present disclosure,the pivots 103, 109, 113 and 115 are configured to allow completedisassembly of the tool. Complete disassembly is important to allow thetool to be fully cleaned and sterilized between uses. Thus, the pivotseach include a pin and retaining ring construction, with the retainingring holding the components together on the pin. Thus, as shown in FIG.1D, the pin 121 can extend through walls of a component, such as theopposite walls 107 a of the lever handle 107 and through a correspondingbore in the component being connected, such as the knife element 102.Retaining rings 122 can engage grooves 123 at the opposite ends of thepin 121 to hold the components together. Alternatively, one end of thepin can have an enlarged head with the retaining ring engaging theopposite end of the pin. When it is necessary to clean and sterilize thetool 100, all of the retaining rings 122 can be removed, all of the pins121 can be removed, and the connected components separated. The knifeelement 102, the fixed handle 104 and the lever handle 107 are thusseparated so that every surface of the components can be effectivelycleaned.

The elements of the bone cutting tool 100 are formed of medical gradestainless steel. The steel is preferably hardened steel capable ofwithstanding the forces required to cut through bone. In the cleaningprocess, the tool is subjected to steam sterilization, which can bedeleterious to the steel. Thus, in one feature of the presentdisclosure, the surfaces of the stainless-steel elements are passivatedto prevent oxidation of the steel elements during sterilization.

Returning to the process steps, and particularly the step of extractingbone marrow, the pieces produced by the bone cutting tool areimmediately placed into a sterile pitcher and submerged in 300-500 mL ofa grind media. In one aspect of the present system and method, the grindmedia uses PLASMA-LYTE™-A as a base with 10 U/mL heparin, 2.5% humanserum albumin (HSA), and 3 U/mL BENZONASE® reagent (a nuclease thatcleaves both DNA and RNA; Merck KGAA Corporation). Heparin is used as ananticoagulant. HSA provides a protein source to prevent cell adherenceand adsorption to surfaces, as well as reactive oxygen scavenging. It isnoted that conventional grind media utilizes DNase™, but for the presentdisclosure BENZONASE® reagent is substituted for DNase™ reagent (QiagenSciences LLC). Whereas DNase™ works only on DNA, modern pharmaceuticalbiotechnology processing relies on enzymes that can cleave all forms ofDNA and RNA, and can reduce the viscosity of the solution in which thecells are suspended. It is noted that IMDM (Iscove's Modified Dulbecco'sMedia) can substitute for the PLASMA-LYTE™-A, since IMDM is suitable forrapidly proliferating high-density cell cultures and ideal forsupporting T- and B-lymphocytes. It is further noted that DENARASE®reagent (a nuclease that cleaves both DNA and RNA; C-Lecta GmbH) isequivalent to BENZONASE® reagent in the same quantity in the presentprocess. Another pitcher of 300-500 mL of grind media is retained forcollecting the bone fragments after grinding, and another supply ofabout 100 mL of the grind media is retained for rinsing through thegrinder during the grinding process to prevent bone fragments fromsticking to the surface of the pitcher of the grinding components.

An electric bone grinder or a purpose-built bone grinder, such as thegrinder of Biorep Technologies Inc, (Miami, Fla.) can be used in anISO-5 environment within an ISO-7 clean room. Bone types are keptseparate if both VB and ilium from the same donor are being processed.The bone is kept submerged in grind media at all times during and afterthe grinding process. Once all of the donor bone pieces are ground, thechamber of the bone grinder is thoroughly rinsed with fresh processingmedia. The bone fragments are discharged from the grinder into thepitcher containing grind media.

The contents of the pitcher are transferred to sterile bags. In the nextstep the contents of the sterile bags are filtered to extract the solidcomponents. In one embodiment, the contents of each bag are passedthrough a series of stainless steel sieves. In this embodiment, a No. 40(425 μm) sieve is stacked on top of a No. 80 (177 μm) sieve, which isseated over a catch-pan to receive the liquid filter contents. Thesterile bags containing the output from the grinder is swirled and thenpoured evenly over the sieve stack or filtration sets. The filteringprocess is observed to ensure that excessive clumping is not occurring,which can signal the presence of soft tissue or other contaminants. Bonefragments retained on the surface of the sieves are distributed evenlyon the sieves and rinsed with 250 mL of fresh processing medium. In oneembodiment, the processing medium used for rinsing is the grind mediadescribed above or PLASMA-LYTE™ with 2.5% HSA. The sieved bone marrowproduct, which can be approximately 1000 mL in a well-performed process,is transferred to sterile packs for subsequent processing and analysis.The contents of each bag are visually inspected to confirm that thecontents do not include any visible bone fragments or soft tissue.

In another embodiment, the contents of each bag are passed through bonemarrow filtration units, as depicted in FIG. 2. In this embodiment, thesystem 150 includes a stand 154 configured to support a sterilecollection bag 152 which contains the bone fragments and media from thegrinding operation described above. The stand includes a containerhanger 155 configured to engage the cap 153 of the sterile bag tosuspend the container. The bottom of the bag includes a dischargeassembly 160 that includes a pre-filter 162 projecting into the body ofthe collection bag. In one specific embodiment the pre-filter 162 is an850 μm filter. The filter 162 is connected to an output tube 164 that isconnected by a container claim 166 to the input line 171 of a firstin-line filter 170. In the specific embodiment, the first in-line filteris a 200 μm or a 500 μm filter. The output line 172 of the first in-linefilter is connected to the input line 176 of a second in-line filter175. The second in-line filter is a 200 μm or a 500 μm filter. The twoin-line filters are initially both 500 μm for a first pass through thefilter system 150. A second rinse is then performed on the grindingswith the two in-line filters being 200 μm. This double-pass filtrationresults in a cleaner suspension and enhances removal of fat from thesuspension. The second in-line filter 175 has an output line 177 thatcan be engaged to a sterile bag, such as bag 152 for the secondfiltration pass. On the second pass through the system, the output line177 of the second in-line filter 175 can be engaged to a container clamp181 of a transfer pack container 180. The transfer pack container can bea 600-2000 mL bag to accommodate the filtered bone marrow product, whichcan be approximately 1000 mL in a well-performed process.

For quality control, a small quantity of bone marrow, such as 0.3 mL, isextracted from the sterile pack 152 using a syringe at an injection site157 and conducting inversion mixing before pulling the sample. Thesample can be tested by a hematology analyzer, such as a SysmexHematology Analyzer, to determine the TNC (total nucleated cell) contentof the sample, as an indicator of the TNC content of the bone marrowbeing subsequently processed.

Fat Removal and Concentration

The bone marrow product collected from the filtering step is essentiallya fatty emulsion. The fat content of the suspension obtained from thesieve filtering approach disclosed above is greater than the fat contentof the suspension obtained from the double-pass filtration system 150.However, in both cases, there is a need to remove the fat content fromthe suspension. The suspension obtained from the filtering step isrecovered into 250 mL bags which are hermetically sealed with tubewelders. Pairs of sterile bags and taring sticks are mounted within acentrifuge with bag ports facing down, and balanced. Volume compensatingplates are used to prevent creasing of the bags during centrifugation.In one embodiment, the bags are centrifuged at 500×g for 15 minutes atroom temperature to concentrate the cells, preferably to 2-3×10⁸/ml.After centrifugation is complete, each bag is individually hung on aring stand. The distinct layers within the bag are visible, with the fatlayer clearly delineated on top of the supernatant with the bone marrowpellet at the bottom, as shown in FIG. 3. A new sterile bag is welded tothe bag removed from the centrifuge. A bag clamp or clip 190 is placedon the bag just below the fat layer, as shown in FIG. 4, to clamp off orsqueeze the bag closed beneath the fat layer. The pellet is then drainedfrom the centrifuge bag into the new sterile bag, with the bag clippreventing passage of the fat layer. The pellet is agitated as it isdrained to resuspend all of the pellet. After about half of the pellethas drained into the new bag, the tubing is closed with a hemostat ortube sealer. The second centrifuge bag is then welded to the new bagcontaining the pellet, and the contents of this second centrifuge bagare drained into the new bag.

The result of this step is new sterile bags containing the bone marrowcentrifuged to remove the fat. These bags of de-fatted bone marrow arethen centrifuged at 500×g for 15 minutes at room temperature, withvolume compensating plates to prevent creasing of the bags. Each bag isremoved and suspended on a ring stand and a waste bag is welded to thebag, and a plasma extractor is used to remove the supernatant into thewaste bag, as shown in FIG. 5. The tubing is clamped with a hemostatwhen the pellet rises or breaks. The tubing is then sealed and severedto remove the pellet-containing bag from the waste bag, which isdiscarded. A Luer connection is welded to the pellet-containing bag. Thepellets from each bag are combined into a bulk bag using a largesyringe. The pellet-containing bags are rinsed into the bulk bag using arinse media. The bulk bag is inverted several times to ensure that allof the pellet is resuspended.

A small quantity of the processed BM, such as 0.5 mL, can be removed forquality control testing for density and cell count. The test sample canalso be evaluated for human leukocyte antigens, CCR5delta 32 mutationand apolipoprotein (APOE), among other things.

Cryopreservation of the Bone Marrow

It is contemplated that each bone donor can yield three or more bags ofbone marrow through the process described above, based on ten vertebraeand the ilium obtained from the donor. If at the end of the process fora given donor three bags of bone marrow are not obtained, the donor canbe flagged as potentially not passing overall quality control. Apredetermined volume of bone marrow in each bag is contemplated, such as70 mL contained in 250 mL bags. This predetermined volume is used tocalculate the volume of freeze media components necessary for efficientcryopreservation of the bone marrow pellet. The freeze media is asolution of a rinse media and a cryopreservation composition. Thecryoprotectant composition can be a permeable media, such as dimethylsulfoxide (DMSO); 1, 2 propane diol; ethylene glycol; glycerol;foramamide; ethanediol or butane 2, 3 diol; and/or a non-permeablemedia, such as hydroxyethyl starch (HES), Dextran, sucrose, trehalose,lactose, raffinose, Ribotol, Mannitol or polyvinylpyrrolidone (PVP).2.5% HSA also provides cryoprotection through oncotic pressure, cellsurface protein stabilization and reactive oxygen scavenging. In apreferred embodiment, the cryopreservation media is DMSO. The rinsemedia can be an electrolyte medium, such as PLASMA-LYTE™, ISOLYTE®, IMDMor other electrolyte solutions suitable for infusion. The freeze mediacan also include concentrations of oxyrase to reduce oxygen content toless than atmospheric, such as to less than 3% of atmosphericconcentrations. The addition of oxyrase produces a hypobaric compositionthat can facilitate cryopreservation.

The freeze media is prepared by mixing the cryoprotectant and the rinsemedia according to the calculated total volume of freeze media neededfor the volume of bone marrow collected in the prior steps. The bagcontaining the bone marrow is placed on a rocker for mixing and thefreeze media is introduced into the bag by syringe. The freeze media isintroduced at a particular rate over a predetermined time. In oneembodiment, the freeze media is added at a rate of 10% of the media perminute, for a time of ten minutes. Once the media as been mixed with theconcentrated bone marrow, a test sample is extracted by syringe. Theremaining mixture of freeze media and bone marrow is injected inpredetermined amounts into separate cryopreservation bags. In oneembodiment, 70 mL of bone marrow mixture is introduced into eachcryopreservation bag and air is drawn out with a syringe. At the end ofthe process, an 8 mL sample can be removed for sterility testing. Eachcryopreservation bag is sealed to create four compartments, which arethen separated for storage in cassettes to be stored in a cryo-freezer.In another embodiment, the separated compartments are stored in apassive cooling box, such as cooling box 200 shown in FIG. 6.

When the test samples from the particular bone marrow batch have beenvalidated for cell count and sterility, the bags of cryopreserved bonemarrow can be further cooled for long-term storage. In one embodiment,the bags are cooled at a controlled rate to prevent damage to the bonemarrow and cells. In one specific embodiment, the bags are cooled at arate of −1 to −40° C. per minute to a temperature suitable for plungingthe bags into liquid nitrogen. A suitable temperature is in the range of−40 to −100° C. Once that temperature has been reached, the bags arecooled further at a more rapid rate to a temperature of below −130° C.for storage. A cryopreservation bag is placed within a correspondingcompartment 201-203 of the cooling box 200 and the overlapping cover 205is closed over the compartments to provide a sealed environment forcryo-preservation of the contents of the bags. The cooling box is placedwithin a cryo freezer such that the cooling box produces a cooling rateof −0.5 to −2° C.°/min, and typically of −1 C.°/min, with nucleationtemperatures above −20° C. The freezing process continues at theprescribed rate until the temperature of the bone marrow reaches asuitable temperature. The suitable temperature for storage of the bagsis a temperature ≤−80° C. or ≤−150° C.

In another embodiment, the bags are cooled in a static chambertemperature as opposed to the controlled rate cryopreservation describedabove. In the passive cooling approach, the cooling box is placed in a−86° C. freezer until the bags reach a stable temperature.

It is contemplated that the cryopreservation storage can be in manyforms. For instance, the cryopreserved bone marrow can be contained inbags of 1 mL to 5 mL volume or vials of 0.1 to 15 mL volumes. In apreferred embodiment, the bags with 70 mL bone marrow are stored in acooling box within a cryogenic freezer.

The cryopreserved bone marrow is cryobanked for later thawing andextraction of desired cells. The thawed bone marrow can be provided fora wide range of treatments including treatment for leukemias, braintumors, breast cancer, Hodgkin's disease, multiple myeloma,neuroblastoma, non-Hodgkin's lymphoma, blood cancers, ovarian cancer,sarcoma, testicular cancer, other solid organ cancer, rheumatoidarthritis, multiple sclerosis, diabetes mellitus, cystic fibrosus,Alzheimer's disease, genetic immunodeficiencies, metabolic disorders,marrow failure syndromes, and HIV. Bone marrow can also be used forinduction of immunotolerance to reduce the potential rejection of animplant obtained from an organ donor. Bone marrow treatments can also beindicated for casualties caused by radiation and certain biologicalweapons.

Bone marrow is a well-known source for mesenchymal stromal/stem cells(MSCs) which can be harvested from previously cryo-banked organ andtissue donor bone marrow using the methods described above. MSCs areself-renewing, multipotent progenitor cells with multilineage potentialto differentiate into cell types of mesodermal origin, such asadipocytes, osteocytes, and chondrocytes. In addition, MSCs can migrateto sites of inflammation and exert potent immunosuppressive andanti-inflammatory effects through interactions between lymphocytesassociated with both the innate and adaptive immune system. MSCs can beused in treating osteogenesis imperfect, cartilage defects, myocardialinfarction, Crohn's disease, multiple sclerosis, autoimmune disease suchas Lupus, liver cirrhosis, osteo arthritis, and rheumatoid arthritis.Matched HSC/MSC units which can be used in co-transplant for treatmentof graft vs. host disease (GVHD), and for hematopoietic stem celltransplant support.

The present method provides a system for extracting and banking bonemarrow for future clinical use according to the process steps describedabove, as summarized in the flowchart of FIG. 7. This method caneliminate the failures of the current methods of matching bone marrowdonors to groups that are tough to match, such as certain minorities.Once the bone marrow is cryopreserved and banked there is no uncertaintyas to the source of the bone marrow, there is no wait for a futurerecipient and the bone marrow is available in large repeatable volumes.

Automated System for Recovery of Bone Marrow

The present disclosure contemplates an automated process for recovery ofthe bone marrow, and even selection of cells from the bone marrow. Inone aspect, an automated system 209 includes sequential stations, asdepicted in FIGS. 8A-8B. The first station 210 of the automated processdebrides the VBs to remove all soft tissue. In contrast to the manualprocess that operates on one VB at a time, the automated process isconfigured to debride an entire donor VB set (which can be at least tenvertebral bodies). The VBs are mounted on a rack or tray 212 that isconfigured to support the vertebral body set from a given donor. Thetray 212 is placed on transfer rails 216 of a housing 215, as shown inFIGS. 9A-9B, with the tray advanced automatically or manually into theinterior of the housing. The housing 215 supports a plurality ofhydrojets 220 that direct high pressure and high velocity jets of salineonto the VBs. In the known manual process, a manual hydrojet, operatingat lower velocities and pressures, directs a stream of detergent ontothe VB. In the manual process, the detergent is needed to clean the VBsof the soft tissue. In contrast, the automated cleaning station 210 ofthe present disclosure uses a saline medium, with the velocity andpressure of the water jets being sufficient to dislodge all soft tissuefrom the VBs. The automated cleaning station of the present disclosureincludes jets configured to produce a direct stream or narrow “V”water/saline jet that generates a high concentrated impact force atvarying distances. To achieve good coverage of the VBs, the deviceincludes many direct jets at close spacing at different orientationsrelative to the VBs, which allows for uniform cleaning independent ofposition of the VB in the device. In the illustrated embodiment of FIG.9A, the hydrojets are provided in an upper 220 and a lower row 221. The“V” jets are aligned at different angles to achieve full coverage of thesurfaces of the VBs. In addition, or alternatively, the hydrojets 220,221 can be configured to oscillate over the tray of VBs to ensurecomplete coverage.

A visualization device 225 is arranged at the outlet of the debridementstation 210 that is operable to visualize and interpret the VBs exitingthe station to determine if all of the soft tissue has been removed, asshown in FIG. 9B. If not, then the VBs are returned along the rails 216back into the housing for further hydrojet processing. It iscontemplated that a controller (not shown) can be provided to controlthe movement of the tray 212 along the rails 216 and to interpret thesignals generated by the visualization device 225. The visualizationdevice can include a camera that obtains an image of the VBs and thecontroller can include imaging software capable of recognizing the softtissue in the acquired image. A dye can be applied to the cleaned VBs atthe end of the hydrojet debridement process, in which the dye isabsorbed by soft tissue but not bone. The dye can thus provide contrastto facilitate differentiation of any remaining soft tissue from thebone. The visualization device 225 can be configured to pan across theVBs, such as by translating along a frame 226 and by translating theframe in order to view the VBs at all angles.

Returning to FIGS. 8A-8B, once it is determined that the VBs are cleanedof all soft tissue, the debrided VBs are then fed by a conveyor 230 toan automated grinding station 240 to produce appropriately sized piecesfor tumbling and final cell extraction. The manual “cubing” processdescribed above can be variable, time consuming, and potentially notsafe for the operator. The automated system includes a grinding stationthat combines the steps of “cubing” the VBs (i.e., cutting the VBs intosmall pieces) and grinding the cubed VBs to reduce the VBs to 2-3 mmpieces. The rails 216 and tray 212 can be configured to deposit thedebrided VBs onto the conveyor 230 which then automatically transfersthe VBs to an input hopper 242 of the grinding station 240, shown inmore detail in FIGS. 10A-10B. The VBs are directed through an initialmill cutter module 244, then through a funnel 246 to a fine mill cuttermodule 248, as shown in FIG. 10A. As shown in FIG. 10B the initial millcutter module 242 includes opposed rotating grinding mills 245 that areseparated by a predetermined gap, such as a 5-8 mm gap, so that theincoming VBs are ground into coarse-sized segments. The coarse groundsegments are fed to the fine mill cutter module 248 in which smallerdiameter grinding mills 249 are provided. The fine grind mills 249 areseparated by a smaller gap, on the order of 2-3 mm, to produce finelyground VB segments. As shown in FIG. 10A, a funnel 246 conveys thecoarse ground segments to the second grinding mill 248, and a funnel 250directs the finely ground VB segments to a collection pan 252 supportedon a plate 253. During the milling operation, a measured volume ofprocessing/resuspension medium with DNAse™ can be directed through theupper hopper, onto grinding cutters. This medium can be manuallyintroduced during the operation of the grinding station 240, or can beautomatically implemented through nozzles incorporated into the hopper242.

The finely ground VB segments and processing medium are collected in thecollection pan 252 and the plate 253 can be moved to a sieve station 260(FIGS. 8A-8B), whether manually or automatically. Once at the sievestation 260 the contents of the pan 252 are dropped into a sievecartridge unit which includes two 12″ diameter filter sieves-a #40 sieve262 on top followed by a finer #80 sieve 264, as depicted in FIG. 11. Afunnel 266 directs the filtered contents to a collection container 268.The grindings retained by the filters are rinsed within the sievestation 260 with processing/resuspension medium that does not includeDNAse™. The liquid bone marrow product in the collection container 268can be analyzed to determine cell content and then concentrated andpackaged in appropriate volumes for cryopreservation, as describedbelow. Alternatively, some or all of the processed bone marrow can befurther processed using automated cell selection approaches forspecialized cell products such as CD34+ cells. Because large volumes ofcells can be recovered from a single organ donor with this approach, onedonor could yield multiple product types. Moreover, since the source isprimary bone marrow (as opposed to G-CSF mobilized peripheral blood) thecell product will endure cryopreservation processing.

In one modification, the output from the grinding station 240 or thesieve station 260 can be automatically fed to a collection bag forcryogenic treatment. In this modification, the lower funnel 250 can beconfigured to direct the contents to a fluid line connected to a sterilebag. A peristaltic pump can engage the fluid line to pump the outputfrom the grinding station to the sterile bag. A similar arrangement canbe engaged to the funnel 266 of the sieve station.

The content of the collection container 268, which is essentially a bonemarrow slurry, is conveyed, either manually or automatically, to anadjacent tumbler station 270 that includes a mechanical tumbler 272 anda large disposable vessel 274 that can contain the entire contents often processed VBs and associated processing/resuspension medium. Thetumbler 272 has a paddle for agitation of the grinding slurry tomechanically liberate cells. When the tumbling cycle is complete, thecontents of the tumbler are poured through a sieve magazine into thevessel 274. The contents of the vessel 274 can be processed further orprepared for cryogenic storage.

Cell Isolation from Bone Marrow

In one aspect of the present disclosure, a method is provided forselecting CD34-expressing (CD34+) cells from deceased donor bone marrowusing density reduced FICOLL® (a neutral, highly branched, high-mass,hydrophilic polysaccharide which dissolves readily in aqueous solutions)and an immunomagnetic CD34+ cell isolation kit. Surprisingly, it hasbeen found that cell isolation using density reduced FICOLL® prior toCD34 selection is beneficial to obtain high purity and viabilityCD45/CD34+ cells from freshly prepared deceased donor bone marrow. Onthe other hand, FICOLL® at conventional density has been found to beoptimal for CD45/CD34+ cell selection from thawed cryopreserved deceaseddonor bone marrow.

Vertebral sections obtained from a recently deceased donor wereprocessed as described above. Thus, in one embodiment, the bone iscleaned of all soft tissue and then cut into small pieces that wereimmediately submerged into 500 mL of grinding media. The grinding mediacan be PLASMA-LYTE™ A injection pH 7.4, multiple electrolytes, injectiontype 1 USP (PLASMA-LYTE™) containing 2.5% human serum albumin (HSA), 3U/ml DENARASE®, and 10 U/ml heparin. The sectioned VB are ground using abone grinder, filtered and rinsed with rinse media (such as PLASMA-LYTE™with 2.5% HSA). The entire cell suspension is centrifuged to concentratecells to 2-3×10⁸/ml and the cell concentration is extracted. A portionor all of the resulting BM preparation can be used immediately for CD34selection, while the remainder can be prepared for cryopreservation. Thecryopreserved portion involves adding a final concentration of 10% DMSOand 5% HSA to the BM cells and bringing the preparation to −86° C.,either by passive cooling or by controlled cooling at a rate ofapproximately −1° C./min, after which the cryopreserved portion isplunged into liquid nitrogen.

For selection of CD34+ cells, either the newly processed BM preparationis used or a previously cryopreserved portion is thawed for use.FICOLL®-Paque PLUS is added to the BM preparation to separate thedesired CD34+ cell component of the bone marrow. It has been found forcell selection from cryopreserved bone marrow that the conventionaldensity for the FICOLL® of 1.077 g/mL produces acceptable results.However, in one aspect of the present disclosure, for cell selectionfrom freshly prepared deceased donor bone marrow the FICOLL® density isreduced from the conventional density. In particular, the density isreduced by mixing FICOLL®-Paque PLUS (density 1.077 g/mL, GE Company)with Plasma Lyte-A Injection pH 7.4 (Baxter Healthcare 2B2544X) inspecific proportions to obtain an overall density of less than 1.077g/ml, particularly 1.063-1.052 g/mL. In one specific embodiment, thedensity of 1.063 g/mL was found to be optimal for isolation of CD34+cells, taking into account quantity, viability and purity of the CD34+cells.

In one embodiment, 5 ml of the 1.063 g/mL density FICOLL® solutions ispipetted into 15-ml centrifuge tubes, and the BM solution generated fromVBs of deceased donors is carefully layered over the FICOLL® gradient.The tubes are centrifuged for 30 min at 400 g without break at roomtemperature. After centrifugation, buffy coat cells are harvestedcarefully, and the cells are washed in phosphate-buffered saline (PBS)containing 0.5% HSA and 2 mM Ethylenediaminetetraacetic acid (EDTA)(MACS buffer, Miltenyi). In one specific embodiment, centrifugation isperformed for 5 min at 400 g, and the resulting cell pellets areresuspended in 10 ml PBS, followed by a second centrifugation for 5 minat 400 g.

Nucleated cells in the isolated buffy coat can be counted using ahematology analyzer, e.g., SYSTEMEX XP-300™. A CELLOMETER® Vision(Nexcellom) or flow cytometer can be used to determine cell counts ofpurified CD34 cells. 20 microliters of AOPI can be added to 20microliters of cells and after mixing total viable cells can bedetermined. The CD34+ cells can be selected by a positive immuneseparation method using a CLINIMACS® system (Miltenyi, BergischGladbach, Germany) or an EasySep™ CD34 kit (Stemcell Technologies,Vancouver, BC, Canada) in accordance with the protocol of themanufacturer. From testing at various FICOLL® densities it has beensurprisingly determined that the lower FICOLL® density contemplated inthe present disclosure (i.e., 1.063-1.052 gm/mL vs. the conventional1.077 gm/mL density) leads to more optimum cell recovery. Optimizationis based on purity, viability and yield of selected CD34 cells. A targetof >90% purity and >90% viable CD34+ cells is preferred. While lowerFICOLL® densities resulted in greater purity and fewer dead cells, itwas surprisingly found that a greater portion of the CD34+ cells presentin the deceased donor whole bone marrow before selection are lost usingthe lower FICOLL® densities to prepare buffy coat. Thus, the highviability and purity of CD45/CD34+ cells achieved at the conventionalFICOLL® density gradient also leads to a large loss in yield(approximately 60% loss of input CD34+ cells).

Thus, in accordance with one aspect of the present disclosure, forfreshly prepared the optimal density of FICOLL® for selection ofCD45/CD34+ cells at >90% purity and viability is less than 1.077 andparticularly 1.063-1.052. This FICOLL® density provides a higher yieldof CD45/CD34+ cells with similar purity and cell viability to theconventional FICOLL® density approach.

In another aspect of the present disclosure, the CD34+ cells can beinitially acquired from a freshly prepared deceased donor bone marrowusing the reduced density FICOLL-Paque described above. The BM can becryogenically frozen and then the CD34+ cells can be acquired laterusing conventional density FICOLL®-Paque. This approach essentiallyallows selective recovery of cells from deceased donor bonemarrow—either before freezing using the modified FICOLL® density orafter freezing and thawing using conventional FICOLL® density.

Recovery of MSCs from Processed Bone Marrow

In another feature of the systems and methods disclosed herein, a methodis provided for recovering mesenchymal stem cells (MSCs) fromenzymatically digested vertebral body (VB) bone fragments that are thebyproduct of the VB grinding and elution steps of the methods describedherein. In this method, a mixture of both collagenase and neutralprotease is used to obtain the highest possible yields of vertebral boneadherent MSC (vBA-MSC). The MSCs can be recovered from cryopreserved VBbone fragments that are later processed according to the presentdisclosure. In one specific aspect, recombinant Clostridium histolyticumcollagenase, comprised of the two active isoforms, is used in effectiveamounts in the MSC extraction process. The mixture of cells liberated bydigesting VB bone fragment is cultured on tissue-coated plastic in thepresence of Mesencult medium to select proliferative vBA-MSC. Freshlydigested preparations as well as different passages of VB-MSC can becharacterized by flow cytometry, colony forming unit-fibroblast (CFU-F)potential, population doubling time (PDT) and trilineage (adipogenic,chondrogenic and osteogenic) differentiation in vitro.

The present disclosure thus contemplates a method for optimizingdigestion and MSC recovery from vertebral bone fragments using acombination of purified collagenase and neutral protease. In onespecific embodiment, the collagenase is DE collagenase (VITACYTE®),which is comprised of purified Clostridium histolyticum collagenase andPaneibacillus polymyxa neutral protease. In accordance with one aspectof the disclosure, optimal neutral protease concentration andcollagenase concentrations (C1 and C2 collagenase) and optimal ratio ofsolution volume (mLs) to bone fragment weight (mgs) are determined.

According to the process, fragments of VB bone are placed incryoprotectant solution comprised of PLASMA-LYTE™, 2.5% human serumalbumin and 10% dimethyl sulfoxide (DMSO) and incubated for 1 hour at 4°C. The solution is removed and the bone fragments cooled at a rate of to−86° C. and then plunged into liquid nitrogen. After 24-48 hours inliquid nitrogen, the bone fragments are thawed rapidly in a water bathset at 37° C. and then washed in saline and digested using thecollagenase/protease solution described above.

The optimal volume-to-weight ratio has been found to be 5:1 at anoptimal incubation time of 2.5 hours. The optimal protease producedneutral protease activity of 19.6 U/mL. On the other hand, it was foundthat total viable MSC cell count is generally insensitive to collagenaseconcentration. It was also found that the yields produced by recombinantcollagenase isoforms C1 and C2 are similar to the yields with purifiedcollagenase, regardless of the C1/C2 ratio. Further details of the MSCrecovery process of the present disclosure are found in the technicalarticle as disclosed in Example 1 of the present application.

Predicting Cell Viability Based on Ischemia Time

As discussed above, ischemia time of the donor bone impacts theviability of the cells extracted using the processes described above.According to the present disclosure, total ischemia is defined as theinterval starting at time of death (the point at which the donor'sarterial system was cross-clamped and circulation ceased) and endingwith the start of the recovery of cells from the bone. For purposes ofstatistical modeling, this total interval can be separated into threesuccessive and mutually exclusive time components: (a) Warm IschemiaTime (WIT)—beginning at time of death and ending either when bones arerecovered and packed on ice or when the body is placed in a cooler; (b)Body Cooling Time (BCT)—beginning when the body is placed in the coolerand ending when bones are packed on ice; and (c) Cold Ischemia Time(CIT)—beginning when bones are packed on ice and ending when processingbegins for extraction of cells, such as HSPCs. Thus, Total IschemiaTime=(WIT)+(BCT)+(CIT). For cases where whole-body cooling is not used,BCT is zero and Total Ischemia Time=(WIT)+(CIT).

In addition to Total Ischemia Time, a variable corresponding toprocessing experience can be incorporated into the viabilitydetermination. It is known that learning curves exert significanteffects on outcomes, so to control for this fact a variable EXP can bedefined as the number of donors processed prior to the currentdonor—i.e., for the i^(th) donor, EXP=i−1. Other variables can includebone type (such as vertebral bodies and ilia), donor sex and donor age.

In one aspect, the outcome variables are: the proportion of a particularcell population, such as CD34+ cells, that are viable, the total numberof colony forming units (CFUs) per 10⁵ nucleated cells detectedfollowing cell processing, and the number of CFU granulocyte macrophages(CFU-GM) detected per 10⁵ nucleated cells.

According to the present disclosure, an ordinary least squares (OLS)beta regression model can be used to predict the outcome variables, withlinear regression models used for CFU and CFU-GM and a beta regressionmodel used for the proportion of viable CD34+ cells, or % CD34+, where0<(% CD34+)<1. The beta regression equation for predicting % CD34+ is:

$\begin{matrix}\begin{matrix}{\eta = {\ln\left\lbrack {pCD3{4^{*}/\left( {1 - {pCD34^{*}}} \right)}} \right\rbrack}} \\{= {\beta_{0} + {\beta_{1}({WIT})} + {\beta_{2}\left( {BCT} \right)} + {\beta_{3}\left( {BCT^{2}} \right)} +}} \\{{\beta_{4}({CIT})} + {\beta_{5}\left( {CIT}^{2} \right)}}\end{matrix} & (1)\end{matrix}$Where:

-   -   pCD34*=[1+100(% CD34+)]/102, which is a transformation of the        variable of interest

-   β₀=Constant (intercept)

-   β₁=Coefficient associated with warm ischemia time (WIT)

-   β₂=Coefficient associated with body cooling time (BCT)

-   β₃=Coefficient associated with body cooling time squared (BCT²)

-   β₄=Coefficient associated with cold ischemia time (CIT)

-   β₅=Coefficient associated with cold ischemia time squared (CIT²)

An inverse link function is applied to the linear predictor η so thatthe result is the expected value of the outcome variable pCD34*, namelythe percentage of viable CD34+ cells expected to be extracted from thedonor bone. The inverse link function is:

$\begin{matrix}{{E\left\lbrack {pCD34^{*}} \right\rbrack} = \frac{\exp(\eta)}{\left\lbrack {1 + {\exp(\eta)}} \right\rbrack}} & (2)\end{matrix}$or substituting Equation (1) above for η:

$\begin{matrix}{{E\left\lbrack {pCD34^{*}} \right\rbrack} = {\frac{\exp(\eta)}{\left\lbrack {1 + {\exp(\eta)}} \right\rbrack} = \frac{\exp\left\lbrack {\beta_{0} + {\beta_{1}\left( X_{1} \right)} + {\beta_{2}\left( X_{2} \right)} + {\beta_{3}\left( X_{3} \right)} + {\beta_{4}\left( X_{4} \right)} + {\beta_{5}\left( X_{5} \right)}} \right\rbrack}{\left\lbrack {1 + {\exp\left( {\beta_{0} + {\beta_{1}\left( X_{1} \right)} + {\beta_{2}\left( X_{2} \right)} + \mspace{25mu}{\beta_{3}\left( X_{3} \right)} + {\beta_{4}\left( X_{4} \right)} + {\beta_{5}\left( X_{5} \right)}} \right)}} \right\rbrack}}} & (3)\end{matrix}$In this embodiment, the mathematical model predicts the proportion ofviable CD34+ cells that can be extracted from the donor bone that hasbeen subjected to the specific ischemia conditions. The value ofE[pCD34*] is between 0 and 1 since it is the ratio of the number ofviable CD34+ cells to the total number of CD34+ cells in the bonesample.

In one embodiment, the coefficients for the beta regression calculationof the predicted % CD34+ have the following values:

-   β₀=3.5000-   β₁=−0.01996-   β₂=−0.181-   β₃=0.007-   β₄=−0.111-   β₅=0.002

where each of the beta coefficients β₀, β₁, β₂, β₃, β₄, β₅ correspond tothe intercept, WIT, BCT, BCT², CIT and CIT², respectively, as describedabove.

The predictions for the total colony forming units CFU and the number ofCFU granulocyte microphages CFU-GM can be obtained using the followinglinear regression model:η=β₀+β₁(WIT)+β₂(BCT)+β₃(BCT²)+β₄(CIT)  (4)

The linear regression model used to determine the CFU outcome variablecan have the following coefficient values:

-   β₀=756.5084-   β₁=−9.10826-   β₂=−95.03639-   β₃=3.45603-   β₄=−4.53349,

where each of the beta coefficients β₀, β₁, β₂, β₃, β₄ correspond to theintercept, WIT, BCT, BCT² and CIT, respectively, as described above.

The linear regression model used to determine the CFU-GM outcomevariable can have the following form:η=+β₁(WIT)+/β₂(BCT)+β₃(CIT)  (5)with the following coefficient values:

-   β₀=104.1805-   β₁=−8.11295-   β₂=−5.52927-   β₃=0.08872.

The foregoing models are base or un-adjusted models that only accountfor the ischemia-based variables and not the experience, bone type,donor sex and donor age variables. A fully adjusted model for % CD34+that accounts for all of the variables can have the following form:η=β₀+/β₁(Experience)+β₂(BoneType)+β₃(WIT)+β₄(BCT)+β₅(BCT²)+β₆(CIT)+β₇(CIT²)  (6)with the following respective values for the coefficients:

% CD34+ β₀ Constant 3.112681 β₁ Experience 0.0095651 β₂ Bone Type (VB= 1) 0.0351495 β₃ Warm Ischemia (WIT) (hrs)^(a) −0.0229737 β₄ BodyCooling (BCT) (hrs) −0.176881 β₅ Body Cooling Squared (BCT²) 0.0062293β₆ Cold Ischemia (CIT) (hrs) −0.101344 β₇ Cold Ischemia Squared (CIT²)0.0013874

The fully adjusted model for CFU is as follows:η=₀+β₁(Experience)+)+β₂(Facility×Experience)+β₃(BoneType)+β₄(WIT)+β₅(BCT)+β₆(BCT²)+β₇(CIT)+β₈(CIT²)  (7)

CFU β₀ Constant 160.6034 β₁ Experience 2.60499 β₂ Facility x Experience5.36988 β₃ Bone Type (VB = 1) 206.9969 β₄ Warm Ischemia (hrs) −3.73481β₅ Body Cooling (hrs) −82.49506 β₆ Body Cooling Squared 2.95994 β₇ ColdIschemia (hrs) 9.55975 β₈ Cold Ischemia Squared −0.12535The coefficient β₁ attempts to quantify the effect of the number ofdonors processed (i.e., experience) on cell quantity and viability. Inthe fully adjusted CFU model, coefficient β₂ corresponds to theexperience at a particular facility based on the assumption thatfacilities can have different learning trajectories. Either or both ofthese coefficients may be modified or even eliminated.

CFU-GM β₀ Constant 88.3589 β₁ Bone Type (VB = 1) 16.71592 β₂ WarmIschemia (hrs) −7.19329 β₃ Body Cooling (hrs) −5.24410 β₄ Cold Ischemia(hrs) 0.10750

Applying these models to observed data can be used to determine theeffect of ischemia time variables on % CD34+, as reflected in the tablesshown in FIGS. 12A-12C, on total CFU, as shown in the tables of FIGS.13A-13C, and on the amount of CFU-GM, as shown in the tables of FIGS.14A-14C. The data in these tables can be used to decide whether aparticular donor bone can yield sufficient cells to warrant furtherprocessing of the donor bone. In other words, the predictive models canbe used to establish ischemia tolerance limits and HSPC qualityacceptance criteria. For instance, with respect to the % CD34+ outcomevariable, predicted values of over 80% may be required in order toconsider the particular donor bone.

The models described above and the examples shown in the tables of FIGS.12a -14C suggest that acceptable levels of HSPC quality are achievabledespite the prolonged ischemia times that are inevitable when bones mustbe procured by geographically-dispersed OPOs and shipped long distancesto processing centers. Even under such conditions, favorablecombinations of warm- and cold-ischemia times can be achieved, enabling% CD34+viabilities in the range of 80-90%. The models also suggest thatrefrigerating the body prior to bone recovery, a practice that is commonin the recovery of tissues, is less beneficial in the context of bonemarrow recovery. For instance, when whole-body cooling was used, CD34+viability averaged 72.75%, whereas when body cooling was not used, theaverage was just under 90%. These models suggest that an optimalpractice would be to dispense with body cooling and move recovered boneas quickly as possible to a cold ischemic environment. The modelsfurther suggest that limiting WIT (warm ischemia time) to less thaneight (8) hours and CIT (cold ischemia time) to less than 40 hoursoptimizes the opportunity to recover meaningful quantities of viablecells from donor bone.

The models disclosed herein predict viability according to the chartshown in FIG. 15 in which an 80% CD34+ cell viability threshold isdetermined to be acceptable. As reflected in the chart, the relationshipbetween warm and cold ischemia times follows a curve from a point atwhich the WIT is 10 hours and the CIT is 18 hours, to a point at whichthe WIT is 1 hour and the CIT is 27 hours.

Further details of the method for predicting cell viability of thepresent disclosure are found in Example 2 of the present application.

The present disclosure should be considered as illustrative and notrestrictive in character. It is understood that only certain embodimentshave been presented and that all changes, modifications and furtherapplications that come within the spirit of the disclosure are desiredto be protected.

EXAMPLES Example 1: Identification and Characterization of a LargeSource of Primary Human Mesenchymal Stem Cells Tightly Adhered to BoneSurfaces of Vertebral Body Marrow Cavities

Therapeutic allogeneic mesenchymal stem/stromal cells (MSC) arecurrently in clinical trials for evaluating their effectiveness intreating many different disease indications. Eventual commercializationfor broad distribution will require further improvements inmanufacturing processes to economically manufacture MSC at sufficientscale required to satisfy projected demand. A key contributor to thepresent high cost of goods (COG) for MSC manufacturing is the need tocreate master cell banks (MCBs) from multiple donors, which leads tovariability in large-scale manufacturing runs. Therefore, theavailability of large single donor depots of primary MSC would greatlybenefit the cell therapy market by reducing costs associated withmanufacturing.

We have discovered that an abundant population of cells possessing allthe hallmarks of MSC is tightly associated with the vertebral body (VB)bone matrix and are only liberated by proteolytic digestion. Here wedemonstrate that these vertebral bone-adherent (vBA) cells possess allthe International Society of Cell and Gene Therapy (ISCT)-definedcharacteristics (e.g., plastic adherence, surface marker expression andtrilineage differentiation) of MSC and, therefore, have termed themvBA-MSC, to distinguish this population from loosely associated MSCrecovered through aspiration or rinsing of the bone marrow (BM)compartment. Pilot banking and expansion was performed with vBA-MSCobtained from 3 deceased donors and it was demonstrated that bank sizesaveraging 2.9×10⁸+1.35×10⁸ vBA-MSC at passage one were obtainable from100 g of digested VB bone fragments. Each bank of cells demonstratedrobust proliferation through a total of 9 passages without significantreduction in population doubling times. The theoretical average totalyield with limited expansion of 4 passages yielded 2 trillion (2×10¹²)cells from a single donor, equating to 30,000 doses at 10⁶ cells/kg foran average 70 kg patient. Thus, we have established a new and plentifulsource of MSC which will benefit the cell therapy market by overcomingmanufacturing and regulatory inefficiencies due to donor-to-donorvariability.

Introduction

The potent activity as well as high expandability of mesenchymalstromal/stem cells (MSC) has generated considerable interest fromcommercial entities in developing “off-the-shelf” allogeneic MSCtherapeutics derived from a limited number of donors. Development of acellular therapy based on allogeneic “universal donors” allows forcontrolled manufacture with careful attention to thoroughly assessingquality (e.g., identity, potency, purity and safety) of eachmanufactured lot at significant cost savings compared to manufacturingindividual lots of autologous cells for individual donors, such ascurrently occurs for chimeric antigen receptor (CAR) T cell therapies.

The challenges inherent to manufacturing cellular therapies scale withthe size of a manufacturing run. Effective doses of MSC for someindications are as high as 1×10⁹ cells per dose, which would requiremanufacturing 10 trillion (10×10¹²) cells per year to affordably meetpotential demand [1-4]. Even at this level of production, with presumedeconomies of scales, the cost of goods (COG) per dose of MSC couldexceed $100,000 [3]. A significant driver of manufacturing costs, whichis amplified proportionately with lot size, is the need to replenishmaster cell banks (MCB) through isolation of MSC from new donors due tothe limited volumes of tissues and fluids that can be safely obtainedfrom healthy volunteers and the limited expansion potential of MSCisolated from each donor [5, 6]. MSC are rare in all tissues,comprising, for instance, 0.001-0.01% of total nucleated cells (TNC) inBM aspirates [7]. Given that BM aspirates from healthy volunteers arelimited for the safety of the donor to 100 ml (50 ml bilaterally fromiliac crests), the total yield of fresh, non-passaged MSC isapproximately 2×10⁴/donor. Expansion to a trillion cells would requireseed stocks of 1×10⁷ MSC in order to limit cell proliferation to 9population doublings [8]. This number is in addition to the cellsreserved for quality control measurements of the expanded MCB andworking cell bank (WCB). Thus, the number of MSC obtainable from eachdonor is more than 3 orders of magnitude less than is optimal for theinitial stages of expansion.

The need to constantly replenish cell banks by obtaining fresh cellsfrom new donors introduces inconsistencies into the manufacturingprocess due to the observed variability between MSC derived fromdifferent donors otherwise matched for attributes such as age and healthstatus [6, 9, 10]. Donor-to-donor variability and the resulting economicimpact on manufacturing costs is substantial. In one study that examinedlarge scale manufacturing of multiple lots of MSC derived from differentdonors it was found that cumulative population doublings between 5different BM donors varied by 1.8-fold during 30 days in culture [9].The result was a >13 day variation in process time to manufacture abatch of 350 million MSC. Besides the logistical burden to coordinatebatch runs, there was a commensurate increased cost of growth medium,which is also a key cost-driver for cell-based therapy manufacturing [1,3, 8]. Furthermore, the authors found that there was >18% difference incolony forming potential and >50% difference in interleukin 6expression, adding an additional complication to quality controlverification of potency for each batch derived from individual donors.Similarly, a single center experience with clinical manufacture of 68batches of MSC from BM recovered from 59 human volunteer donors observedpopulation doubling times that varied by over 2-fold (46.8141 hours),averaging 71.7 hours, yielding final batch numbers of MSC ranging from1.9×10⁷ to 5.43×10⁹ (average 5.46×10⁸) [10].

Besides imposing a direct economic burden of increasing COG permanufacturing run, there is also a regulatory burden with associatedcosts resulting from the need to refresh cell banks. The MCB serves asthe reserve of starter cultures for all manufacturing runs using cellsfrom a particular donor. The regulatory requirements for quality andsafety assessments of the MCB are costly and time consuming [11]. Of thethree overarching parameters (e.g., safety, potency and identity)required to assess suitability of a manufactured cell therapy product,potency as it relates to individual donor characteristics, is mostproblematic due to the changing profile that occurs with expansion, asdescribed above. This is particularly the case as MSC populations nearthe limits of expansion and enter into senescence which severely limitstheir potency [12]. For these reasons, population doubling limits is animportant factor for regulatory authorities; albeit, one that is notcommonly addressed in filings with FDA [13].

Reducing the economic and regulatory burden of generating multiple MCBlots annually to fulfill the need for large scale manufacturing requiresidentifying large depots of unmanipulated MSC. Potential solutions couldcome from abundant tissues harboring MSC that are normally discardedfollowing routine medical procedures or are obtainable post-mortem.Adipose-derived stem/stromal cells (ASC) are obtained from electiveprocedures that commonly yields liters of tissue and have recently beenextensively investigated; however, primarily for autologous uses [14,15]. Isolation directly from medullary cavity-containing bones obtainedthrough medical procedures or cadavers yields higher percentages of MSC(−0.04%) than are present in aspirates, most likely reflecting lack ofperipheral blood contamination [16]. Total nucleated cell counts of−5×10⁹ have been obtained from BM of vertebral bodies (VB) recoveredfrom deceased organ donors, with each VB containing −2×10⁶ MSC, or−2×10⁷ total MSC per typical spinal 9 VB segment recovered [17]. Inaddition, the ilia, sternum, ribs and heads of long bones are sources ofBM from which MSC can be recovered [18-20]. Thus, the VB compartment ofBM alone from a typical deceased donor yields >3 orders of magnitudemore MSC than can be obtained from a health human donor.

In addition to cells obtained by eluting or aspirating BM, anotherpopulation of MSC is tightly associated with medullary cavity bonestructures [21-23]. First identified in rodent long bones, bone-adherentMSC (BA-MSC) have subsequently been isolated from human bone fragmentsobtained from long bone condyles and vertebrae [24]. We have discoveredanother source of MSC, termed vertebral BA-MSC (vBA-MSC), which remainattached to fragments of VB bone after extensive washing to remove BMcells and can only be liberated by digestion with proteases. Thefrequency and functionality of vBA-MSC is equivalent to that in elutedVB BM-MSC. Here we present these data and establish a new source of MSCthat could be used in large scale manufacturing processes to producebatches totaling of over a quadrillion cells from an individual donor;thus, satisfying the most optimistic levels of demand for decades andovercoming a current impediment to commercial scale production [2, 8].

Materials and Methods

Sources of Tissues and Cells

Vertebrae were recovered following cardiac death of brain-dead organdonors after obtaining informed consent for research use from survivingfamily members. Each recovered vertebra was assigned a unique ID. Theinclusion criteria for donor selection were brain death, age between 12and 55 years, non-septicemic, and disease and pathogen free. Live donoraspirated BM from three healthy volunteers was purchased from Lonza(Walkersville, Md.). Expanded live donor MSC, cryopreserved at passage2, were purchased from Lonza. Relevant donor characteristics arepresented in Table 1.

Deceased Donor Tissue Procurement and Transport

Previously developed clinical recovery methods [16, 25] combined withsubsequent experience in the ongoing VCA transplant immune toleranceclinical trial (ClinicalTrials.gov Identifier: NCT01459107) at JohnsHopkins University formed the basis for the procurement and transportprotocols. A streamlined organ procurement agency (OPO) recoveryprocedure, combined with dedicated kits and centralized training onrecovery and shipment procedures were employed. Recovered bones wereshipped to Ossium Health (Indianapolis, Ind.). Vertebral sections wereprocured by six different OPO partners: Gift of Hope (Itasca, Ill.);Donor Alliance (Denver, Colo.); Iowa Donor Network (North Liberty,Iowa); Mid America Transplant (St. Louis, Mo.); and Nevada Donor Network(Las Vegas, Nev.). Bones were recovered by OPO personnel using anosteotome and mallet under an IRB approved protocol fromresearch-consented organ and tissue donors. Recovered bones were wrappedin lap sponges and towels soaked in saline to ensure moisture retentionduring shipment. Wrapped specimens were shipped overnight on wet ice toone of the two processing facilities.

Manual Debriding

Upon receipt, in a Biological Safety Cabinet, soft tissue was manuallydebrided using scalpels and gouges. Once visible, the pedicles wereremoved using either a tissue processing band saw or a Stryker System 6Saw (Stryker, Kalamazoo, Mich.) leaving only the connected vertebralbodies. Vertebral bodies were separated and intervertebral disc and softtissue was removed with a scalpel. Care was taken to ensure that thecortical bone was not breached to preserve and protect the hypoxiccancellous bone marrow throughout the entire debriding process.

Using custom-made surgical grade stainless-steel anvil shears, VBs werecut into approximately 5 cm³ pieces small enough to be fragmented with abone grinder. The pieces were immediately submerged into 500 mLprocessing medium comprised of PLASMA-LYTE™ A pH 7.4 (Baxter Healthcare,Deerfield, Ill.), containing 2.5% human serum albumin (HSA; OctapharmaUSA Inc., Hoboken, N.J.), 3 U/ml BENZONASE® (EMD Millipore, Burlington,Mass.), and 10 U/ml heparin (McKesson, Irving, Tex.).

Grinding and Elution

A bone grinder (Biorep Technologies. Inc, Miami, Fla.) was assembled ina biological safety cabinet. A two liter stainless steel beakercontaining approximately 250 mL of fresh processing medium was placedunder the grinding head to catch bone chips and media flow-through. Astainless steel plunger was used to aid in pushing pieces through thegrinder. Rinsing through the grinder with processing medium preventedbone pieces from drying out and sticking to the chamber. Once all bonepieces were ground, the chamber was thoroughly rinsed with freshprocessing medium. The final volume in the stainless-steel beaker wasone liter.

Filtering was performed using bone marrow collection kits with flexiblepre-filter and inline filters (Fresenius Kabi, Lake Zurich, Ill.). Allbone grindings and media were carefully transferred to the bone marrowcollection kit. The grindings were gently massaged to allow for optimalcell release from grindings. The media was then filtered using two 500μm and two 200 μm filters. The bone grindings are rinsed using two 500mL washings with rinse media. Rinse media was PLASMA-LYTE™ with 2.5%HSA. All bone marrow was then collected in a collection bag wheresamples were taken for experiments.

Digestion Protocol for MSC Isolation

Bone fragments (either 1 or 100 g) were transferred to either 50 mlconical centrifuge tubes or 250 ml WHIRL-PAK® bags. A solution of DE10collagenase (2 mg/ml; VITACYTE®, Indianapolis, Ind.) was added to thebone fragments at a ratio of 5:1 (volume:weight). The tubes and/or bagswere transferred to a shaking incubator and incubated for 2 hr at 37° C.while shaking at 125 rpm. Protease activity was neutralized by adding 2%STEMULATE® (Cook Regentec, Indianapolis, Ind.) and suspensions werefiltered through a 70 μm cap filter into 50 ml conical screw cap tubes.The filter-retained bone fragments were washed with 25 ml Dulbecco'smodified phosphate buffered saline (DPBS) solution containing heparin(10 U/ml) and BENZONASE® (100 U/ml) which was combined with the originalfiltrate. Tubes were centrifuged at 350×g for 5 minutes, supernatantaspirated, and the pellets were resuspended in 10 ml DPBS. Thesuspension was centrifuged again at 350×g for 5 minutes, the supernatantwas aspirated, and the pellet was resuspended in DPBS for analysis.

Isolation of MSC from Iliac and VB BM

An 1 ml aliquot of concentrated, eluted BM was removed and pipetted intoa 50 ml conical vial along with 49 ml DPBS. The vial was centrifuged at300×g for 10 minutes, supernatant aspirated, and the pellet resuspendedin 10 ml Rooster-Nourish medium (Rooster Bio, Frederick, Md.). Cellswere counted and cultured as described below.

Cell Counting

A CELLOMETER® Vision (Nexcellom, Lawrence, Mass.) was used to determinetotal viable cell counts. 20 μl VIASTAIN™ AOPI reagent (Nexcelom) wasadded to an Eppendorf tube containing 20 μl of cells. Once mixed, 20 μlof the solution was added to a CELLOMETER® slide and total cells, livecells, and viability were calculated.

Cell Culture

Fresh cells were plated in CellBIND® T-225 flasks at a density of 25,000viable cells/cm² in Rooster-Nourish medium (Rooster Bio, Frederick,Md.). Nonadherent cells were removed after the first media change onday 1. Media was then changed every 3-4 days until colonies were ˜80-90%confluent. Cells were released with TRYPLE™ (ThermoFisher Scientific,Waltham, Mass.). Passaged cells were plated at a density of 3,000cells/cm² but otherwise followed the same protocol as freshly platedcells.

Generation of MCBs from three donors (DDS, DD6 and DD7) was performed inCellBind® Hyperflasks. Fresh, primary digests were initially plated at25,000 viable cells/cm² as above. Cells were released with TRYPLE™ andexpanded one more passage to form the MCB. The bulk of passage 1 cellswere resuspended in cryopreservation medium (CryoStor CS10; BioLifeSolutions, Bothell, Wash.) and stored in the vapor phase of liquidnitrogen.

Cells were passaged up to ten times in a medium composed of DMEM(Cat#10567014, ThermoFisher, USA), ascorbic acid (248 μM; Cat# A2218,Sigma, USA), recombinant basic fibroblast growth factor (10 ng/ml;Cat#233-GMP-025, R&D Systems, USA) and recombinant epidermal growthfactor (10 ng/ml; Cat#236-GMP-200, R&D Systems, USA). Cells at 70-80%confluency were harvested and total cell counts were obtained. A portionof the cells was replated at 3000 cells/cm² in triplicate wells of asix-well plate, with media changes every 3-4 days.

Phenotypic Analysis of MSC Via Flow Cytometry

At passages 2, 3 and 4, 1.8 μl of the following singlefluorescently-conjugated antibodies or dye, CD3, CD14, CD19, CD31, CD34,CD45, HLA-DR, CD73, CD90, CD105, Stro-1, and 7AAD (Table S1), were addedto different wells of a 96-well V-bottom plate. 100 μl of MACS (MiltenyiBioTec) buffer and 100 μl of cells (200,000 cells) were added to eachwell containing an antibody. The plate was incubated at 4° C. for 30minutes shielded from light and afterward, the plate was centrifuged for5 minutes at 300×g. Cells were washed and resuspended in 200 μl of MACSbuffer. An ACEA Biosciences NovoCyte NOVOCYTE® 2060R flow cytometer wasused for data collection and data was analyzed using NOVOEXPRESS®software (Acea Biosciences San Diego, Calif.).

Trilineage Differentiation of MSC

MSCs after passage 1 were seeded in wells of a 12-well plate containing3 ml MESENCULT™ (Stem Cell Technologies, Vancouver, B.C.) each at8.0×10⁴, 4.0×10⁴, and 2.0×10⁴ for chondrogenesis, adipogenesis, andosteogenesis differentiation. A well of containing 4.0×10⁴ MSCs was alsoplated as a control. After incubating for 2 hours, MESENCULT™ in thechondrogenesis well was replaced with STEMPRO™ chondrogenesis medium(Thermo Fisher Scientific, Waltham, Mass.). After one day, MESENCULT™ inthe adipogenesis and osteogenesis wells was aspirated and replaced withSTEMPRO™ adipogenesis medium and STEMPRO™ osteogenesis medium,respectively. Respective differentiation media were replenished every 3days as well as MESENCULT™ in control wells. After 14, 12, and 16 days,wells containing chondrocytes, adipocytes, and osteocytes, respectively,were aspirated of media, washed twice with DPBS, fixed with 4% formalinfor 30 minutes, washed once with DPBS, and stained. Alcian Blue, whichstains chondrocyte proteoglycans blue, in 0.1 N HCl was added to thechondrocyte well for 30 minutes, the stain was aspirated, the well waswashed three times with 0.1 N HCl and neutralized with distilled water,and chondrocytes were visualized under an inverted light microscope(Nikon). Oil Red 0, which stains adipocyte fat globules red, was addedto the adipocyte well for 15 minutes, the stain was aspirated, the wellwas washed three times with distilled water, and adipocytes werevisualized under an inverted light microscope. 2% Alizarin Red, whichstains osteocyte calcium deposits red, was added to the osteocyte wellfor 3 minutes, the stain was aspirated, the well was washed three timeswith distilled water, and adipocytes were visualized under an invertedlight microscope. All differentiated cells were qualitatively analyzedby visualization of color and phenotypic profile.

Population Doubling Time

Population doubling time was determined at each passage by using theformula: t*log(2)/log(T1/T0), where t is the time (hours) betweeninitial plating and cell harvest at 90% confluency, T1 is the cell countat harvest and TO is the initial count at seeding.

CFU-F Assays

For freshly digested cells, 5 ml MESENCULT™, 20 μl Amphotericin B, and100 μl Gentamycin were added to three wells of a 6-well plate. 2.5×10⁵,5.0×10⁵, and 7.5×10⁵ cells were added to the first, second, and thirdwells, respectively. Plates were placed in the incubator until colonieswere 90% confluent or up to 12 days. Media was changed every 3-4 daysfor 14 days. Plates were washed twice with DPBS, and 2 ml methanol wasadded to each dish for 5 minutes to fix the cells. After 5 minutes, themethanol was decanted, the plate was allowed to air dry and colonieswere stained with a 1% crystal violet solution. Colonies containing >50cells were scored. Passaged cells were assayed similarly except thatcells were plated at densities of 32 cells/cm², 65 cells/cm², and 125cells/cm².

T Cell Suppression Assays

Suppression of T cell activation was performed according to previouslypublished protocols with minor modifications [26]. Briefly, peripheralblood mononuclear cells were isolated from whole blood (10 ml) byFICOLL® (GE, Chicago, Ill.) separation and resuspended in DPBS. Themajority of cells were labeled with carboxyflourescein succinimidylester (CFSE; Sigma, St. Louis, Mo.) and frozen until used [27]. Passage2 or 3 MSCs, in some cases pre-stimulated with 100 ng/ml interferon-g(IFNγ; RnD Systems, Minneapolis, Minn.) for 18-24 hours, wereresuspended in RoosterNourish (RoosterBio, Frederick, Md.) and added toa 96 well flat bottom plate at 4×10⁵, 1×10⁵, 5×10⁴, 2.5×10⁴, 1.5×10⁴,5×10³ cells/well. RoosterNourish was added to each well until the volumewas 200 pl/well. The plate was placed in a 37° C. incubator with 10% CO2at 5% humidity for at least two hours to allow MSCs to attach.Cryopreserved PBMCs were quickly thawed and resuspended at aconcentration of 4×10⁶ cells/ml in Eagle's minimal essential medium(EMEM; Stem Cell Technologies; supplemented with 10% FBS, 100 pg/mlPenStrep, 2 mM L-glutamine, and 100 pM b-mercaptoethanol). The mediumwas aspirated from the plates containing MSC and 100 pl of PBMCs wereadded to all wells containing MSCs as well as wells without MSCs. Tcells were stimulated by adding 100 pl of supplemented EMEM with 40pg/ml phytohemagglutinin (PHA; Sigma-Aldrich, St. Louis, Mo.) to eachwell containing MSCs and PBMCs. Control wells containing labeled andunlabeled PBMCs alone were also included, half of which were stimulatedwith PHA and half which were not. The plate was returned to theincubator. After 4 days, PBMCs from each well were removed and labeledwith 5 pl CD3-PE and 5 pl of 7AAD before performing flow cytometry

Statistics

GRAPHPAD PRISM® version 8 was used for statistical analysis (Student's tTest). An P value <0.05 was considered significant.

Results

A typical vertebral column (typically T8-L5) before and after removingsoft tissues, separating VBs and then fragmenting to sizes ofapproximately 1.5 cm³ is shown in FIG. 16.

FIG. 16A to FIG. 16D show processing of a typical vertebral column toisolate vBA-MSC. The vertebrae (typically T8-L5) is cleaned of softtissue (FIG. 16A) before separating vertebral bodies (VBs) and removingdisks and remaining soft tissues (FIG. 16B). VBs are ground toapproximately 1.5 cm³ fragments (FIG. 16C) before enzymatic digestion torelease adherent cells. Plastic adherent vBA-MSC form typical spindleshapes in culture (FIG. 16D; passage 2 cells). Plastic adherent vBA-MSCpossessed a typical spindle-shaped morphology in culture (FIG. 16D).

Cells from donors were expanded through passage 4 (the initial platingwas considered passage 0) and assayed by flow cytometry. vBA-MSC atpassages 1-4 possessed negligible levels of hematopoietic stem andprogenitor cell surface markers CD14, CD19, CD34 and CD45 and expressedlow to non-existent amounts of human leukocyte antigen DR (HLA-DR) (FIG.17A). In FIG. 17A, passage 1, 2, 3 and 4 vBA-MSC from 3 different donors(DD1, DD2 and DD23; Table 1) were analyzed for surface antigenexpression using fluorescently-conjugated antibodies and flow cytometry.The percentage of cells (gated on whole cells using side and forwardscatter) after culturing for each passage is shown. Levels of PECAM1(CD31)-expressing cells (typically endothelial cells and monocytes) werealso low (<7%) at passage 2 (data not shown). Conversely, passagedvBA-MSC were uniformly positive for CD73, CD90 and CD105. Thus, vBA-MScpossess the characteristic MSC surface marker profile [28]. In addition,a variable portion (approximately 20% or lower, depending on the passagenumber) of the population also expressed the multipotential MSC surfacemarker Stro-1 [29-32].

Chondrogenic, adipogenic and osteogenic potentials of passage 3 vBA-MSCwere determined for each donor. Each of the vBA-MSC isolatesdemonstrated the potential to differentiate into chrondrocytes,adipocytes and osteocytes (FIG. 17B to FIG. 17E). Passage 3 vBA-MSCgrown in expansion medium (FIG. 17B) or induced to undergo either (FIG.17C) chondrogenesis, (FIG. 17D) adipogenesis or (FIG. 17E) osteogenesiswere imaged after staining for chrondocytes (alcian blue), adipocytes(oil red 0) or osteocytes (alizarin red), as described in Materials andmethods. Images are representative of results with the 3 differentdonor-derived vBA-MSC. Magnification for all 20×. A portion of bothfreshly isolated (i.e., never plated) as well as passaged vBA-MSCdemonstrated high degrees of clonal proliferation, as determined bycolony forming unit-fibroblast (CFU-F) potentials.

The average CFU-F frequency in freshly digested VB bone fragments was0.01+0.004% (mean+standard deviation), which is similar to the frequencyof proliferative MSC in whole BM (FIG. 18) [7].

FIG. 18 shows colony forming unit-fibroblast (CFU-F) potential ofisolated vBA-MSC from 3 different donors (DD1, DD2 and DD3; Table 1) andplated immediately after isolation by digestion (fresh) or after 1 or 2passages (P1 and P2). Either 5×10⁵ (fresh) or 624 (passaged) total cellsfrom each of 3 donors were plated in triplicate wells of a 6 well plateand incubated for 14 days with media changes every 3-4 days. Theproliferative cells were maintained with cell culture, forming coloniesat a frequency of 37+3.4% and 27+1.2% after one and two passages,respectively.

Suppression of T cell activation is one of the best studied therapeuticproperties of MSC, providing the rationale for testing in clinicaltrials of inflammatory disorders [33, 34]. vBA-MSC from the threedifferent donors dose-dependently suppressed T cells activation with PHA(FIG. 19).

PBMC isolated from the blood of a single donor were labeled withcarboxyfluorescein diacetate succinimidyl ester (CSFE). vBA-MSC wereallowed to adhere 2 hours in 96 well plates before washing and adding4×10⁵ PBMC. In some experiments IFN-′y (100 ng/ml) was added 18-24 hoursbefore adding PBMC. T cells were stimulated for 4 days with PHA. Cellswere recovered from the plates and analyzed by flow cytometry afterlabeling with anti-CD3-PE antibodies. The percentage of activated Tcells is plotted.

Maximum suppression at a 1:1 ratio of vBA-MSC to peripheral bloodmononuclear cells (PBMC) was 89+7%. A slight but non-significantincrease in suppression at all ratios was observed by pre-treatingvBA-MSC with IFN-′y for 18-24 hours prior to performing the suppressionstudies. Treatment with IFN-′y has been shown to stimulate suppressivefunctions of MSC, with enhanced effects on senescent cells [12]. Thelack of an enhanced response to IFN-′y priming indicates that culturedvBA-MSC retain full immunomodulatory capacity.

FIG. 19 shows representative flow plots for PBMCs alone, without andwith PHA activation as well as PBMC and MSC after PHA activation areshown. Each data point represents the mean of 3 different experimentswith 3 different donors (DD1, DD2 and DD3). Error bars represent thestandard deviation. P>0.05 for comparisons at all PBMC:vBA-MSCratios+/−IFN-y.

The immunophenotypic profile of plastic adherent vBA-MSC, trilineagedifferentiation capacity and CFU-F potential as well as immunomodulatoryproperties confirm the classification of these cells as MSC according tothe International Society of Cell and Gene Therapy (ISCT) publishedguidance [28]. To further establish their equivalency to MSC obtainedfrom BM, a comparison was performed between vBA-MSC and MSC isolatedfrom central BM (FIG. 21). Both commercially available previouslyexpanded live donor BM-MSC (Ex LD BM-MSC), obtained cryopreserved atpassage 2, as well as MSC freshly isolated from live donor aspirated BM(LD BM-MSC) were used. In addition, MSC isolated from deceased donor VBBM (DD vBM-MSC) was also included in the comparison. MSC from threedonors for each source were expanded to passage 2 and cryopreserved.Upon subsequent thawing, cells were passaged once prior to performingthe analyses. MSC from all four sources demonstrated essentiallyidentical immunophenotypic cell surface marker profiles, with very lownumbers of cells that expressed CD14, CD19, CD34, CD45 and HLA-DR, and,conversely, nearly all cells expressed CD73, CD90 and CD105.

Surface marker expression of passage 3 cells was characterized by flowcytometry. The different sources of MSC were: deceased donor vBA-MSC (DDvBA-MSC); deceased donor vertebral body bone marrow-derived MSC (DDBM-MSC); living donor aspirated BM MSC (LD BM-MSC); and living donoraspirated BM MSC obtained from a commercial sources at passage 2 (LD ExBM-MSC). There were no differences in surface marker expression betweencell types. MSC from each source grew rapidly in culture through 5passages (the longest period examined) with no differences in populationdoubling times (PDTs) at passages 4 and 5. Comparison of populationdoubling times (PDT) from passages 2 to 3, 3 to 4, and 4 to 5. LD ExBM-MSC grew significantly (*, P<0.05) slower between passage 2 and 3than either vBA-MSC and LD BM-MSC. No difference in PDT was observed inthe subsequent 2 passages.CFU-F assays were performed as described inFIG. 18 for passaged cells. Formation of CFU-F was significantly lower(*, P<0.05) for passage 2 LD Ex BM-MSC compared to the other threesources of MSC, also at passage 2. Each bar represents the mean+sd fromthe 3 donors for each MSC source. The specific donors were: LD BM-MSC(donors LD1, LD2 and LD3); LD Ex BM-MSC (donors LD4, LD5 and LD6);vBM-MSC and vBA-MSC (donors DD1, DD2 and DD3). Donor characteristics arelisted in Table 1.

Later passages were not compared for CFU-F potential. Finally,trilineage differentiation potentials were compared and it was foundthat each MSC population formed adipocytes, chrondrocytes and osteocytesin vitro at qualitatively the same frequencies (FIG. 20). Here, cellswere culture and induced to undergo differentiation for each cell typeas described in FIG. 17. There was no qualitative difference in eitheradipogenic, chondrogenic and osteogenic potential of passage 3 cellsfrom any of the four sources. Images are representative from experimentswith the 3 different donors for each source of MSC. Magnification isindicated for each image.

The potential clinical translational utility of vBA-MSC was assessed byperforming a pilot-scale manufacturing run to examine feasibility ofbanking and expanding large numbers of cells from individual donors.Fragments of VB from 3 different donors (DDS, DD6 and DD7) wereisolated, digested to isolate vBA-MSC, and expanded to passage 1 to forma master cell bank were. The amount (100 g) corresponds to approximatelyone-third of the total VB bone fragment weight obtainable from typicaldonors. A portion of the passage 1 vBA-MSC from each donor was expandedto passage 9. A MCB at passage 1 from each donor, containing an averageof 2.9×10⁸+1.35×10⁸ vBA-MSC, was prepared and the bulk cryopreserved,while the remainder was cultured over multiple passages, tracking totalcell yields at each passage (FIG. 21A). Passage 1 was considered to beoptimal for an MCB, displaying essentially the same surface makerprofile and CFU-F potential as later passages (FIG. 17 and FIG. 18). Asingle further expansion to passage 2 was enough to produce an WCBcontaining 5.17×10⁹+4.3×10⁹ vBA-MSC. Based on observed populationdoublings, two expansions of the entire WCB were sufficient tomanufacture over a trillion cells. The PDT remained nearly constantbetween passages 2 and 9, without indications of diminishing growth rateat the upper passage number.

However, there were differences in PDTs between donors (FIG. 21B).Population doublings (PD) were calculated based on initial numbers ofcells plated and the number recovered after each plate reached 80%confluency before replating the cells and was used to determine thetheoretical total cell yield after each passage. Theoretical totalyields at passages 2-9 were obtained by exponentiating (base 2) the PDcalculated for each passage and multiplying by the cumulative cellnumber from each preceding passage. Each donor vBA-MSC was plated intriplicate for each passage. The coefficient of variation (CV) betweencell numbers obtained from each well was <15%.

Based on the observed PDTs for each donor, starting with a seed stock of2 million vBA-MSC, it would require 23, 36 and 29 days to manufactureone trillion cells from the three different donors. These times werecalculated using 2-dimensional tissue culture flasks and would likelydiffer in bioreactors.

Discussion

The transformative potential of MSC to treat a wide variety of medicaldisorders has been idealized for over a decade; yet, despite manydemonstrations of this potential in preclinical and early-stage clinicaltrials, no MSC-based therapies have achieved success in late-stage,registration (commonly Phase 3 in the U.S.) clinical trials, although afew have received approval for limited indications in relatively smalljurisdictions. The reasons for the slow progress in approvals andresulting commercialization of therapeutic MSC despite intensedevelopment efforts by multiple entities is certainly multifactorial. Inhindsight, it appears that attempts to manufacture MSC at large scalethrough adopting processes and procedures from the highly successfulbiopharmaceutical sector might have been a contributory factor [35, 36].There are many differences between manufacturing products derived fromcells versus the cells themselves. Biopharmaceuticals are produced usingimmortalized cell lines having the ability of nearly unlimitedexpansion, allowing the generation of large MCBs from a single seedstock. Conversely, the limited availability and expansion potential ofMSC requires generating multiple MCB from different donors each year ata disproportionately higher manufacturing costs and regulatory burden[36].

We present here a viable solution to reducing these burdens through theidentification and characterization of a large depot of MSC fromdeceased donor vertebral bones. Based on the analysis presented here,vBA-MSC are phenotypically and functionally equivalent to MSC obtainedfrom central BM. The cells express typical MSC markers (CD73, CD90 andCD105) and lack expression of hematopoietic stem and progenitor cellmarkers as well as express very low levels of HLA class II proteins.Like BM-MSC, vBA-MSC possess the potential to clonally expand and can beinduced to undergo trilineage differentiation. Passaged vBA-MSC arefully fit to suppress T cell activation, demonstrating no difference inactivity with prior stimulation by IFN-g. The differences in PDT andCFU-F of passage 3 (but not later passages) expanded BM-MSC obtainedfrom a commercial source most likely reflects a slower recovery fromcryopreservation at passage 2. All MSC were grown to passage 2 andcryopreserved in an effort to maintain comparability; however, thecommercial source of expanded BM-MSC were likely grown in a differentmedium and frozen in a different cryopreservation medium. Thus, thecells experienced a lag upon thaw and growth to passage 3 which was notevident in subsequent passages.

Cell bank sizes averaging 2.4×10⁸ MSC were obtainable from 100 g ofdigested VB bone fragments from each of 3 donors. Each bank was expandedthrough a total of 9 passages without a significant reduction ofpopulation doubling time. The theoretical yield with full expansion ofeach donor through 9 passages was 4×10¹⁹ (40 quintillion) cells,equating to over 500 billion doses at 10⁶/kg for an average 70 kgpatient. Inevitably, actual total cell yields will be lower due toinefficiencies inherent in large scale manufacturing and requirementsfor testing; nonetheless, the COG for production of large batches from asingle donor would likely be much less than for equivalent scales ofmanufacturing from multiple donors. The savings in direct manufacturingcosts would be in addition to the reduced regulatory burden with using asingle donor source for all manufacturing campaigns. The next step invalidating the potential cost savings with vBA-MSC will be to performscaled-up manufacturing runs, which are currently in progress.

We are presently exploring the question of why some populations of MSCare easily dislodged or possibly free floating in the BM, while othersremain tightly adhered to the bone/connective tissue matrix and can onlybe liberated by enzymatic digestion. Determining differences, if theyexist, is complicated by the relatively low frequency of these cells,making them problematic to characterize using common analytical tools,such as flow cytometry, without first expanding in culture, whichinduces phenotypic and functional alterations [37-45]. One previousreport found that freshly isolated enzymatic digests of pelvic regiontrabecular bone contained 15-fold higher CFU-F than aspirated BM [24];however, we did not find a similar difference between freshly isolatedvBA-MSC and BM-MSC. To better understand dissimilarities, if any,between the populations, we are pursuing single cell RNA sequencing(scRNA-Seq) of vBA-MSC transcriptomes [46, 47]. We are also continuingto characterize the therapeutic potential of vBA-MSC by studying thesecretome and extracellular vesicles produced by these cells.

In summary, based on the data presented here, the fundamental nature ofvBA-MSC does not appear to differ from aspirated BM-MSC; therefore,these cells could potentially be seamlessly substituted for therapeuticapplications at a significant savings in manufacturing and regulatorycosts. Additionally, other markets requiring large numbers of MSC couldalso benefit from an abundant source of primary cells. These includetissue engineering and manufacture of products derived from MSC, such asexosomes, as well as biomedical research applications and the emergingapplications of cosmeceuticals and bioengineered materials. Each ofthese markets is expected to grow substantially over the next decades,driving combined demand for MSC in excess of 10 sextillion (1×10²¹)cells annually by 2040 [2]. Future high demand for MSC across all thesemarkets could be entirely met by vBA-MSC obtained from the abundant andsteady supply of deceased donor medullary cavity containing bones fromthe 10,000 organ donors and a further 40,000 tissue donors each year inthe U.S. alone.

TABLE 1 Description of donors used in Example 1 DD1 vBA-MSC, vBM-MSC 22M Caucasian DD2 vBA-MSC, vBM-MSC 13 M Caucasian DD3 vBA-MSC 35 MHispanic DD4 vBA-MSC, vBM-MSC 19 M Hispanic DD5 vBA-MSC 17 M CaucasianDD6 vBA-MSC 14 M Caucasian DD7 vBA-MSC 23 M Caucasian LD1 LD BM-MSC 20 FAfrican American LD2 LD BM-MSC 23 F African American LD3 LD BM-MSC 28 MAfrican American LD4 Ex LD BM-MSC 24 F African American LD5 Ex LD BM-MSC36 M African American LD6 Ex LD BM-MSC 25 M African AmericanAbbreviations: DD, deceased donor; LD, live donor; BM, bone marrow

TABLE S1 Description of antibodies and dyes used Antibody FluorophoreClone Isotype Source CD3 PE UCHT1 IgG1-PE BD CD14 PE MφP9 IgG2b-PE BDCD19 PE 4G7 IgG1-PE BD CD31 PE MBC78.2 IgG1-PE BD CD34 PE 8G12 IgG1-PEBD CD45 APC F10-89-4 IgG2a-APC Caprico FILA-DR APC L243 IgG2a-APCCaprico CD73 PeCy7 TY/11.8 IgG1-PeCy7 Biolegend CD90 FITC F15-42-1IgG1-FITC Caprico CD105 APC 43A3 IgG1-APC Biolegend Stro-1 APC STRO-1IgM-APC ThermoFisher 7AAD¹ — — — Invitrogen ¹Abbreviations: 7-AAD,7-aminoactinomycin; PE, phycoerythrin; APC, allophycocyanin; PeCy7,phycoerythrin-cyanin 7; FITC, fluorescein isothiocyanate.

REFERENCES

-   1. Lipsitz, Y. Y., et al., A roadmap for cost-of-goods planning to    guide economic production of cell therapy products.    Cytotherapy, 2017. 19(12): p. 1383-1391.-   2. Olsen, T. R., et al., Peak MSC-Are We There Yet? Front Med    (Lausanne), 2018. 5: p. 178.-   3. Pereira Chilimia, T. D., F. Moncaugeig, and S. S. Farid, Impact    of allogeneic stem cell manufacturing decisions on cost of godds,    process robustness and reimbursement. Biochemical Engineering    Journal, 2018. 137: p. 132-151.-   4. Simaria, A. S., et al., Allogeneic cell therapy bioprocess    economics and optimization: single-use cell expansion technologies.    Biotechnol Bioeng, 2014. 111(1): p. 69-83.-   5. Harrison, R. P., N. Medcalf, and Q. A. Rafiq, Cell    therapy-processing economics: small-scale microfactories as a    stepping stone toward large-scale macrofactories. Regen Med, 2018.    13(2): p. 159-173.-   6. Mizukami, A., et al., Technologies for large-scale umbilical    cord-derived MSC expansion: Experimental performance and cost of    goods analysis. Biochemical Engineering Journal, 2018. 135: p.    36-48.-   7. Pittenger, M. F., et al., Multilineage potential of adult human    mesenchymal stem cells. Science, 1999. 284(5411): p. 143-7.-   8. Chilima, T. D. P., T. Bovy, and S. S. Farid, Designing the    optimal manufacturing strategy for an adherent allogeneic cell    therapy. BioProcess International, 2016. 14(9): p. 24-32.-   9. Heathman, T. R., et al., Characterization of human mesenchymal    stem cells from multiple donors and the implications for large scale    bioprocess development. Biochemical Engineering Journal, 2016.    108: p. 14-23.-   10. Lechanteur, C., et al., Large-scale clinical expansion of    mesenchymal stem cells in the GMP-compliant, closed automated    Quantum® cell expansion system: Comparison with expansion in    traditional T-flasks. Stem Cell Research & Therapy, 2014. 4(8): p.    1-11.-   11. Wuchter, P., et al., Standardization of Good Manufacturing    Practice-compliant production of bone marrow-derived human    mesenchymal stromal cells for immunotherapeutic applications.    Cytotherapy, 2015. 17(2): p. 128-39.-   12. Chinnadurai, R., et al., Immune dysfunctionality of replicative    senescent mesenchymal stromal cells is corrected by IFNgamma    priming. Blood Adv, 2017. 1(11): p. 628-643.-   13. Mendicino, M., et al., MSC-based product characterization for    clinical trials: an FDA perspective. Cell Stem Cell, 2014. 14(2): p.    141-5.-   14. Lockhart, R. A., J. A. Aronowitz, and S. Dos-Anjos Vilaboa, Use    of Freshly Isolated Human Adipose Stromal Cells for Clinical    Applications. Aesthet Surg J, 2017. 37(suppl 3): p. S4-S8.-   15. Dykstra, J. A., et al., Concise Review: Fat and Furious:    Harnessing the Full Potential of Adipose Derived Stromal Vascular    Fraction. Stem Cells Transl Med, 2017. 6(4): p. 1096-1108.-   16. Donnenberg, A. D., et al., Clinical implementation of a    procedure to prepare bone marrow cells from cadaveric vertebral    bodies. Regen Med, 2011. 6(6): p. 701-6.-   17. Ahrens, N., et al., Mesenchymal stem cell content of human    vertebral bone marrow. Transplantation, 2004. 78(6): p. 925-9.-   18. Cox, G., et al., High abundance of CD271(+) multipotential    stromal cells (MSCs) in intramedullary cavities of long bones.    Bone, 2012. 50(2): p. 510-7.-   19. Rybka, W. B., et al., Hematopoietic progenitor cell content of    vertebral body marrow used for combined solid organ and bone marrow    transplantation. Transplantation, 1995. 59(6): p. 871-4.-   20. Soderdahl, G., et al., Cadaveric bone marrow and spleen cells    for transplantation. Bone Marrow Transplant, 1998. 21(1): p. 79-84.-   21. Blashki, D., et al., Mesenchymal stem cells from cortical bone    demonstrate increased clonal incidence, potency, and developmental    capacity compared to their bone marrow-derived counterparts. J    Tissue Eng, 2016. 7: p. 2041731416661196.-   22. Siclari, V. A., et al., Mesenchymal progenitors residing close    to the bone surface are functionally distinct from those in the    central bone marrow. Bone, 2013. 53(2): p. 575-86.-   23. Yusop, N., et al., Isolation and Characterisation of Mesenchymal    Stem Cells from Rat Bone Marrow and the Endosteal Niche: A    Comparative Study. Stem Cells Int, 2018. 2018: p. 6869128.-   24. Jones, E., et al., Large-scale extraction and characterization    of CD271+ multipotential stromal cells from trabecular bone in    health and osteoarthritis: implications for bone regeneration    strategies based on uncultured or minimally cultured multipotential    stromal cells. Arthritis Rheum, 2010. 62(7): p. 1944-54.-   25. Gorantla, V. S., et al., Development and validation of a    procedure to isolate viable bone marrow cells from the vertebrae of    cadaveric organ donors for composite organ grafting.    Cytotherapy, 2012. 14(1): p. 104-13.-   26. Li, M., et al., Therapeutic Delivery Specifications Identified    Through Compartmental Analysis of a Mesenchymal Stromal Cell-Immune    Reaction. Sci Rep, 2018. 8(1): p. 6816.-   27. Quah, B. J., H. S. Warren, and C. R. Parish, Monitoring    lymphocyte proliferation in vitro and in vivo with the intracellular    fluorescent dye carboxyfluorescein diacetate succinimidyl ester. Nat    Protoc, 2007. 2(9): p. 2049-56.-   28. Dominici, M., et al., Minimal criteria for defining multipotent    mesenchymal stromal cells. The International Society for Cellular    Therapy position statement. Cytotherapy, 2006. 8(4): p. 315-7.-   29. Gronthos, S., et al., Molecular and cellular characterisation of    highly purified stromal stem cells derived from human bone marrow. J    Cell Sci, 2003. 116(Pt 9): p. 1827-35.-   30. Simmons, P. J. and B. Torok-Storb, Identification of stromal    cell precursors in human bone marrow by a novel monoclonal antibody,    STRO-1. Blood, 1991. 78(1): p. 55-62.-   31. Dennis, J. E., et al., The STRO-1+ marrow cell population is    multipotential. Cells Tissues Organs, 2002. 170(2-3): p. 73-82.-   32. Bensidhoum, M., et al., Homing of in vitro expanded Stro-1− or    Stro-1+human mesenchymal stem cells into the NOD/SCID mouse and    their role in supporting human CD34 cell engraftment. Blood, 2004.    103(9): p. 3313-9.-   33. Galipeau, J., et al., International Society for Cellular Therapy    perspective on immune functional assays for mesenchymal stromal    cells as potency release criterion for advanced phase clinical    trials. Cytotherapy, 2016. 18(2): p. 151-9.-   34. Squillaro, T., G. Peluso, and U. Galderisi, Clinical Trials With    Mesenchymal Stem Cells: An Update. Cell Transplant, 2016. 25(5): p.    829-48.-   35. Galipeau, J. and L. Sensebe, Mesenchymal Stromal Cells: Clinical    Challenges and Therapeutic Opportunities. Cell Stem Cell, 2018.    22(6): p. 824-833.-   36. Jossen, V., et al., Manufacturing human mesenchymal stem cells    at clinical scale: process and regulatory challenges. Appl Microbiol    Biotechnol, 2018. 102(9): p. 3981-3994.-   37. Banfi, A., et al., Replicative aging and gene expression in    long-term cultures of human bone marrow stromal cells. Tissue    Eng, 2002. 8(6): p. 901-10.-   38. Baxter, M. A., et al., Study of telomere length reveals rapid    aging of human marrow stromal cells following in vitro expansion.    Stem Cells, 2004. 22(5): p. 675-82.-   39. Bork, S., et al., DNA methylation pattern changes upon long-term    culture and aging of human mesenchymal stromal cells. Aging    Cell, 2010. 9(1): p. 54-63.-   40. Bruder, S. P., N. Jaiswal, and S. E. Haynesworth, Growth    kinetics, self-renewal, and the osteogenic potential of purified    human mesenchymal stem cells during extensive subcultivation and    following cryopreservation. J Cell Biochem, 1997. 64(2): p. 278-94.-   41. Digirolamo, C. M., et al., Propagation and senescence of human    marrow stromal cells in culture: a simple colony-forming assay    identifies samples with the greatest potential to propagate and    differentiate. Br J Haematol, 1999. 107(2): p. 275-81.-   42. Muraglia, A., R. Cancedda, and R. Quarto, Clonal mesenchymal    progenitors from human bone marrow differentiate in vitro according    to a hierarchical model. J Cell Sci, 2000. 113 (Pt 7): p. 1161-6.-   43. Redaelli, S., et al., From cytogenomic to epigenomic profiles:    monitoring the biologic behavior of in vitro cultured human bone    marrow mesenchymal stem cells. Stem Cell Res Ther, 2012. 3(6): p.    47.-   44. Moravcikova, E., et al., Proteomic Profiling of Native    Unpassaged and Culture-Expanded Mesenchymal Stromal Cells (MSC).    Cytometry A, 2018. 93(9): p. 894-904.-   45. Bara, J. J., et al., Concise review: Bone marrow-derived    mesenchymal stem cells change phenotype following in vitro culture:    implications for basic research and the clinic. Stem Cells, 2014.    32(7): p. 1713-23.-   46. Choi, Y. H. and J. K. Kim, Dissecting Cellular Heterogeneity    Using Single-Cell RNA Sequencing. Mol Cells, 2019. 42(3): p.    189-199.-   47. Hwang, B., J. H. Lee, and D. Bang, Single-cell RNA sequencing    technologies and bioinformatics pipelines. Exp Mol Med, 2018.    50(8): p. 96.

Example 2: The Relationships of Ischemia Time and Whole-Body Cooling tothe Quality of Hematopoietic Stem and Progenitor Cells Recovered fromthe Bone Marrow of Deceased Organ Donors

Deceased organ donors represent an untapped source of therapeutic bonemarrow (BM), which can be recovered in 3-5 times the volume of thatobtained from living donors, tested for quality, cryopreserved, andbanked indefinitely for future on-demand use. However, a challenge for afuture BM banking system built to a genetically diverse scale will be tomanage the prolonged ischemia times that inevitably occur when bonesprocured at geographically-dispersed locations are shipped to distantfacilities for processing. The goals of this study were: (a) toquantify, under realistic and scaled procurement and shippingconditions, the relationship between ischemia time and the quality ofhematopoietic stem and progenitor cells (HSPCs) derived fromdeceased-donor BM; (b) to identify ischemia-time boundaries beyond whichHSPC quality is adversely affected, and (c) to investigate whole-bodycooling as a tactic for preserving cell viability and function. Boneswere analyzed from 62 deceased donors following exposure to variousperiods of warm ischemia time (WIT), cold ischemia time (CIT) and bodycooling time (BCT). Regression models were developed to quantify theindependent associations of WIT, CIT and BCT in relation to theviability and function of recovered HSPCs. Results demonstrate thatunder “real-world” scenarios: (a) combinations of warm and cold ischemiatimes favorable to the recovery of high-quality HSPCs are readilyachievable (e.g., CD34+viabilities in the range of 80-90% were commonlyobserved); (b) cooling the body prior to bone recovery is detrimental tocell viability (e.g., CD34+ viability <73% with, vs. >89% without, bodycooling); and (c) vertebral bodies (VBs) are a superior source of HSPCscompared to ilia (IL) (e.g., % CD34+viability >80% when VBs were thesource vs. <74% when IL were the source). Our quantitative models can beused to formulate ischemia-time tolerance limits and HSPCquality-acceptance criteria, and to inform an emerging BM banking systemseeking to institute data-driven industry standards.

Introduction

Deceased-donor bone marrow (BM) represents a large, untapped source ofhematopoietic stem and progenitor cells (HSPCs) that could becryopreserved and banked for future on-demand use in bone marrowtransplant (BMT) procedures. The appeal of BM banking is based in parton the recognition that HSPCs could be immediately available duringsurges in demand as, for example, following a mass casualty event suchas a nuclear disaster resulting in widespread bone-marrow failure [1,2]. Interest has been further solidified by recent successes withinducing durable or operational immune tolerance through infusing donorBM cells to establish transient mixed chimerism and/or peripheralimmunomodulation in recipients of solid organ and vascular compositeallograft (VCA) transplants [3-5]. A bank of BM from deceased organdonors establishes a repository for future tolerance inductionprocedures using delayed protocols which have been successful innon-human primates [6, 7].

Cryopreservation and banking of BM from deceased organ donors willrequire the establishment of BM banks similar in concept to umbilicalcord blood banks. As with cord blood, it is well-established that BMremains biologically functional following cryopreservation and can serveas a genetically diverse, on-demand source of stem cell grafts [8-11].Importantly, the national Organ Procurement Organization (OPO) network,which has been active in the United States (US) for over 50 years,provides an existing, well-functioning infrastructure for procuring andtransporting bone tissue recovered from deceased donors. However,organizing an organ-donor BM procurement and banking system thatcapitalizes on existing OPO infrastructure will require coordinatedefforts involving the recovery and safe shipment of biological materialto specialized BM cell-processing centers appropriately scaled forclinical production.

A critical issue, which typically has not been viewed as significant inthe case of living BM donors, is the ischemia time that inevitably isintroduced during recovery and shipment of bones recovered from deceaseddonors. Before a clinical production system can be brought to scale, itwill be necessary to determine how variations in warm- and cold-ischemiatimes influence the quality of HSPCs derived from bones recovered atgeographically dispersed locations and shipped long distances tocentralized processing facilities. And it will be necessary to establishupper tolerance limits for both warm- and cold-ischemia, which, ifexceeded, would likely render the quality and functionality of HSPCsunacceptable for therapeutic use.

Additionally, the impact of whole-body cooling in the context ofdeceased-donor bone recovery and shipment needs to be better understood.Current tissue-banking guidelines in the US allow tissues to berecovered from deceased donors up to 24 hours following asystole,provided the body is refrigerated within 12 hours of cardiac arrest[12]. However, body cooling is a variable that has not been investigatedsystematically in relation to the recovery of BM, and it is one that mayrequire different criteria than those established for tissue recovery.

Here we present our results for the first time, which quantify theassociations of ischemia time and whole-body cooling with the quality ofHSPCs recovered from cadaveric vertebral bones. Our analyses show thathigh-quality, functional HSPCs can be obtained from deceased donors evenafter recovered bones are subjected to cumulative warm- andcold-ischemia times exceeding 40 hours, provided that body cooling,which is shown to be detrimental to viability, is avoided. Thesefindings should be useful in establishing warm- and cold-ischemia-timetolerance limits and HSPC quality acceptance standards for BM derivedfrom deceased organ donors.

Methods

Study Design

This is a pragmatic observational field study designed to model theeffects of ischemia and body-cooling times on the viability and functionof HSPCs recovered from the BM of deceased organ donors [13]. The studywas designed to produce observations that can be generalized and appliedin routine practice settings. The study's external validity(generalizability) was enhanced by securing the participation ofmultiple OPOs operating under normal field conditions. Except forspecial training related to the details of bone recovery and shipment(see below), usual procurement conditions were in effect. Because theOPOs were geographically dispersed, the collected data cover the fullspectrum of ischemia times likely to be seen under “real-world”procurement and shipping scenarios.

Donor Tissue Procurement and Transport

Previously developed clinical recovery methods combined with subsequentexperience in the ongoing VCA transplant immune tolerance clinical trialat Johns Hopkins University (ClinicalTrials.gov Identifier: NCT01459107)formed the basis for the procurement and transport protocols [4, 14-16].However, these protocols required optimization and validation to ensurethat multiple OPOs could reliably operationalize them in a manner thatallowed for the production of consistent yields of functionally viableHSPCs after recovery and transport of bones across a broad geography. Tothat end, a streamlined OPO recovery procedure, combined with dedicatedkits and centralized training on recovery and shipment procedures wereemployed.

Recovered bones were shipped to one of two processing facilities locatedin Centennial, Co. (Facility A) or Indianapolis, Ind. (Facility B).Vertebral sections (Facility A and B) and/or ilia (Facility A, only)were procured by six OPOs: Gift of Hope (Itasca, Ill.); Donor Alliance(Denver, Colo.); Iowa Donor Network (North Liberty, Iowa); Mid AmericaTransplant (St. Louis, Mo.); and Nevada Donor Network (Las Vegas, Nev.).Bones were recovered by OPO personnel using an osteotome and malletunder an IRB approved protocol from research-consented organ and tissuedonors. Unprocessed bones were wrapped in lap sponges and towels soakedin saline and placed in triple-sealed bags to ensure moisture retentionduring shipment. Wrapped specimens were shipped overnight on wet ice toone of the two processing facilities.

Manual Debriding

Upon receipt, in an ISO 5 clean room (Facility A) or a Biological SafetyCabinet (Facility B), soft tissue was manually debrided using scalpelsand gouges. Once visible, the pedicles were removed using either atissue processing band saw or a Stryker System 6 Saw (Stryker,Kalamazoo, Mich.) leaving only the connected vertebral bodies. Using aboning knife (Facility B) or tissue processing band saw (Facility A),vertebral bodies were separated at the intervertebral disc. Remainingintervertebral disc and soft tissue was removed with a scalpel, leavingclean, separated VBs. Ilium soft tissue was removed with gouges and ascalpel. Care was taken to ensure that the cortical bone was notbreached to preserve and protect the hypoxic cancellous BM throughoutthe entire debriding process.

Using a saw and/or anvil shears, VBs and ilium were cut into 5 cm³pieces small enough for fragmenting with a bone grinder. The pieces wereimmediately submerged into 500 mL processing medium (Iscove's ModifiedDulbecco's Medium containing 100 U/mL DNase™, 10 U/mL heparin, and 2.5%human serum albumin). IMDM is suitable for rapidly proliferatinghigh-density cell cultures and ideal for supporting T- andB-lymphocytes. DNase™ is essential for the mitigation of cell clumpingas a result of DNA release from dying cells and post-mortem stress ondeceased donor derived BM. Heparin was used as an anticoagulant. HSAprovided a protein source to prevent cell adherence and adsorption tosurfaces.

Grinding and Elution

An electric bone grinder was assembled in an ISO-5 cleanroom (FacilityA), and a purpose-built bone grinder (Biorep Technologies Inc., Miami,Fla.) was assembled in a Biological Safety Cabinet (Facility B). Ineither facility, a 2 L stainless steel beaker containing 100 mL of freshprocessing medium was placed under the grinding head to catch bonefragments and media flow-through. Bone types were kept separate if bothVB and IL from the same donor were processed. Processing medium was usedto rinse the grinder throughout the process to prevent bone from dryingand sticking to the chamber. Once all bone pieces were ground, thechamber was thoroughly rinsed with fresh processing media. The finalvolume in the stainless-steel beaker was typically around 750 mL

Stainless steel sieves were stacked with a No. 40 (425 μm) on top of aNo. 80 (177 μm) and seated over a round catch-pan (WS Tyler, St.Catherines, ON). The stainless-steel beaker was swirled and poured overthe sieves. Bone fragments were distributed evenly on top of the sieveand rinsed with 250 mL of fresh processing medium. The sieved BMproduct, approximately 1000 mL, was transferred to a sterile pack forfinal analysis.

Nucleated Cell Counts

An aliquot of BM extract was subjected to red blood cell lysis withammonium chloride RBC lysis buffer. In a 15 mL conical tube, 4 mL of 9%ammonium chloride was added to 1 mL of BM cell suspension and incubatedfor 5 minutes at room temperature. Following incubation, the lysedsample was filled to the top of the tube with IMDM containing 100 U/mLDNase™, 10 U/mL heparin, and 2.5% HSA processing medium. The lysedsample was centrifuged at 300×g for 5 minutes and decanted. The samplewas then washed with 15 mL of processing medium, centrifuged at 300×gfor 5 minutes, and decanted. Finally, the lysed cells were re-suspendedwith 1 mL of the same processing medium. Viable nucleated cell countswere obtained using Trypan blue and a hemocytometer.

Flow Cytometry

Flow Cytometry was performed using an ACEA Biosciences NovoCyte 2060Requipped with 488 nm and 640 nm lasers. ISHAGE methods were used toenumerate CD45+ and CD34+ cells [16]. 500 μL of lysed bone marrowextract was stained for 15 minutes with 2 μL each of CD45-FITC,CD34-APC, 7-AAD, and Annexin-PE. All conjugated antibodies werepurchased from BD Biosciences and 7-AAD was obtained from TonboBiosciences. Cells were also stained with individual conjugateantibodies for controls and compensation. After incubating for 15minutes, cells were washed with Dulbecco's phosphate buffered saline,centrifuged, and re-suspended in 500 μL of PBS. These samples were rundirectly on the flow cytometer and analyzed using the ISHAGE gatingscheme [16] For each sample 100,000 total events gated on the Singletsgate were collected.

Colony Forming Unit (CFU) Assay

The concentration of RBC lysed cell suspension was first adjusted to 10⁵viable cells/mL with processing medium before adding 250 μL to 2.5 mL ofsemisolid medium, METHOCULT™ Optimum (Stem Cell Technologies, Vancouver,Canada) and then vigorously vortexed to achieve adequate mixing. A 3 ccsyringe was used to remove at least 2.2 mL of METHOCULT™ containingcells. 1.1 mL was dispensed into each of two 35 mm non-tissue culturetreated dishes. The dishes were covered and tilted to ensure coating ofentire plate surface with METHOCULT™. The two dishes were placed insidea larger 100 mm petri dish with a third uncovered 35 mm dish containingsterile DI water to humidify the plate. Plates were incubated for 14days at 37° C., 5% CO2 before scoring colonies.

Numbers of Donors and Bone Marrow Samples Utilized for StatisticalModeling

Seventy-five bones from 62 donors were initially received at one of thetwo BM processing facilities. The numbers of samples with complete datarecords differed depending on the outcome being modeled. Table 1provides a breakdown of the numbers received and the numbers withcomplete data available for statistical modeling by outcome.

TABLE 2 Numbers of donors and bones available for analysis by outcomeTotal with Complete Data for Analysis: Modeled Outcome Donors Bones VBIL % CD34+ 62 75 52 23 CFU-TOTAL/10⁵ 54 67 42 25 CFU-GM 10⁵ 54 66 41 25Definition of Ischemia Time

Total ischemia was defined as the interval from time of death (when thedonor's arterial system was cross-clamped and circulation ceased) tostart of BM recovery at the processing facility. For purposes ofstatistical modeling, this total interval was separated into threesuccessive and mutually exclusive time components: (a) Warm IschemiaTime (WIT): Beginning at time of death and ending either when bones wererecovered and packed on ice or when the body was placed in a cooler. (b)Body Cooling Time (BCT): Beginning when the body was placed in thecooler and ending when recovered bones were packed on ice. (c) ColdIschemia Time (CIT): Beginning when recovered bones were packed on iceand ending when processing began for extraction of HSPCs. By thesedefinitions, Total Ischemia Time=(WIT)+(BCT)+(CIT). When body coolingwas not used BCT was coded zero and Total Ischemia Time=(WIT)+(CIT).Ischemia times were considered the main variables of interest inpredictive outcome models.

Definition of Experience

Because this was the first series in our hands in which BM was processedfrom cadaveric bone, we hypothesized that HSPC quality might improvewith learning as we gained more processing experience. This hypothesisrests on long-established research demonstrating that learning curvesexert significant effects on outcomes and costs in both industrialmanufacturing [17] and medical practice settings [18-20]. To control forlearning, we created a variable, EXPERIENCE, defined as the number ofdonors processed prior to the current one. For the i^(th) donor,EXPERIENCE was coded i−1, to indicate that EXPERIENCE is always one lessthan the serial number of the current case being processed. BecauseFacility A began processing BM five months before Facility B, andbecause Facility B had the advantage of participating in and learningfrom cases processed at Facility A, we hypothesized that the twofacilities would have different learning trajectories. To account forthis possible difference, each facility's experience was codedseparately. To identify the facilities in the model, we coded FacilityA=1 and Facility B=0. The effect of EXPERIENCE was initially estimatedin separate regression models and subsequently incorporated as acovariate in final adjusted models to control for the effect of learningon outcomes.

Other Covariates

Other variables tested in statistical models were: (1) BONE TYPE,vertebral bodies (VB) and ilia (IL), (representing the two sources of BMcells, coded VB=1; IL=0); DONOR SEX (percent male); and DONER AGE(years). These additional covariates were treated as exogenous factorsand were included in final models only if they were statisticallysignificant or they improved the model's performance.

Outcome Variables

Outcomes were defined according to three quality measures as hallmarksof potential in vivo utility: (a) The proportion of recovered CD34+cells that were viable (% CD34+) as determined by 7-AAD, (b) The totalnumber of colony forming units (CFUs) per 10⁵ total nucleated cells(TNC) plated (CFU-TOTAL), and (c) The number of CFUgranulocyte-macrophages detected per 10⁵ nucleated cells (CFU-GM).

Summary Statistics

Donor and processing-facility characteristics, ischemia times, andoutcome measures were summarized as means or percentages as appropriate.Crude (unadjusted) comparisons were made between FACILITIES (A vs. B),BONE TYPE (VB vs. IL), and BODY COOLING (Yes or No) usingindependent-groups t-Tests or z-tests for proportions.

Statistical Modeling

The associations of ischemia times with outcomes were initiallyinvestigated in unadjusted regression models using only ischemia timesas predictors. Additional models were then estimated to determine theseparate associations of EXPERIENCE with outcomes. Finally, the effectsof ischemia were evaluated in multivariable models that controlled forthe potential influences of FACILITY, EXPERIENCE, BONE TYPE, DONOR SEX,and DONOR AGE. Separate models were estimated for each of the threeoutcomes of interest (% CD34+, CFU-TOTAL, and CFU-GM).

Ordinary least-squares (OLS) linear regression was employed to test arange of candidate models, including models incorporating two-wayinteractions, as well as logarithmic and second-order polynomial terms.From these candidates, the best reduced models were selected based onthe following criteria. (a) Models with the greatest explanatory power(highest R² values) were favored. (b) Parsimonious models that explainedthe greatest percentage of variation with the fewest predictors werefavored. The adjusted R², which guards against over-specification bypenalizing models containing greater numbers of predictors [21], wasused as a comparative indicator of explanatory power in selecting themost parsimonious models. Models that achieved the highest R² valueswhile simultaneously maintaining or increasing the adjusted R² werefavored. (c) Models with greater precision, as indicated by relativelysmaller standard errors associated with both the model and modelcoefficients were favored. (d) Models with the best fit, as judged by anassessment of residual plots, were favored. Residuals were plotted andexamined visually for discernable patterns, and confirmed quantitativelyby regressing residuals onto observed values to uncover possibleinteractions or underlying curvilinear relationships. Because % CD34+ isa proportion limited to the closed unit interval, [0≤(% CD34+)≤1], wefound that traditional OLS linear regression produced unrealistic fittedvalues exceeding these interval boundaries. To correct for this, wesubstituted beta regression for linear regression in models of % CD34+[22]. Beta regression is useful in situations where the responsevariable is a rate or proportion measured on a continuous scale andbounded by minimum and maximum values. We modeled a transformedvariable, pCD34*=[100×(% CD34+)+1]/102, which satisfies thedistributional assumption of beta regression that the outcome variablemust be restricted to the open interval, [0≤(% CD34+)≤1]. So thatpredicted values could be reported in their original percentage units,beta regression results were back transformed to obtain:Pred(% CD34+)=[102×(Pred(pCD34*))−1/100]

(A technical description of the beta regression model may be found inthe below Technical Appendix A).

Model Validation

All models were validated using leave-one-out bootstrap cross-validation[23], accomplished by randomly omitting one observation with replacementfrom the dataset and re-estimating the model from the remainingobservations. The resulting model was then used to predict the omittedobservation. This procedure was repeated 200 times, yielding 200 modelswith predicted values, model coefficients, standard errors and 95%confidence intervals. Model parameters were summarized as averages ofthe 200 bootstrapped models. Since bootstrap models are naïve to theomitted observations, this form of validation serves as an estimate ofthe predictive accuracy likely to be seen when the original model isused to predict new observations [21]. Model coefficients are reportedfor the original models and compared with averaged coefficients+95%confidence intervals from the 200 cross-validated models.

Results

Sample characteristics are provided in Table 2 and the distribution oftotal ischemia times as well as individual ischemia-time components WIT,CIT and body cooling (BCT) for each of the 62 donors are shown in FIG.22. The majority of donors (77.2%) were male. Average donor age was 41.2years. Mean ischemia times in hours (+standard errors) were 3.6+0.4 forWIT, 7.9+0.9 for BCT, and 19.6+1.2 for CIT. The mean total ischemia timewas 31.0+1.2 hours. An average of 2.43+0.64% CD45dim CD34+HSPC wererecovered from the BM specimens, of which an average 79.3+3.0% wereviable cells. BM contained an average 250.3+49.48 CFU-Total per 10⁵total nucleated cells (TNC) plated and 38.2+7.78 CFU-GM per 10⁵ TNCplated.

TABLE 3 Sample characteristics. Numbers are those associated with the %CD34+ model. Mean/Percent +Std Error Min Max Bone Type (% Vertebrae)65.2% 5.32% — — Donor Sex (% Male) 77.2% 0.54% — — Donor Age (yrs) 41.21.6 13 64 Experience* 26.9 1.2 0 53 Warm Ischemia (hrs) 3.6 0.4 0.0513.4 Body Cooling (hrs) 7.9 0.9 0 22.5 Cold Ischemia (hrs) 19.6 1.2 7.467.8 Total Ischemia (hrs) 31.0 1.2 15.3 70.5 Outcomes % CD34+ viability79.3% 3.0% 15.1% 100% (n = 75) CFU-TOTAL/10⁵ cells 250.3 49.5 0 1,850 (n= 67) CFU-GM/10⁵ cells 38.2 7.8 0 282 (n = 66) *Average number of casesprocessed prior to the current caseUnadjusted Comparisons

Comparisons of FACILITY, BONE TYPE, and BODY COOLING are displayed inFIG. 23. The distributions of donor age and donor sex did not differsignificantly by FACILITY, BONE TYPE, or whether BODY COOLING was used.

Facilities differed significantly in the distribution of BONE TYPE (VBcomprised 27% of the bones processed at Facility A versus 100% atFacility B), which occurred because Facility B was structured to receiveVBs only. Facility A also had more experience (Facility A=53 bonesprocessed vs. Facility B=24 bones processed; p<0.00001), significantlylonger WITs (Facility A=3.55 hours vs. Facility B=2.13 hours; p=0.003),and significantly shorter CITs (Facility A=19.55 hours vs. FacilityB=28.38 hours; p=0.004). The two facilities did not differ in either BCTor total ischemia time. Outcomes differed only for CFU-GM counts, withFacility A having significantly lower counts than Facility B (28.38 vs.64.31, respectively; p=0.04). The two facilities did not differsignificantly in the percentage of viable CD34+ or CFU-TOTAL. Facilitydifferences were controlled in final regression models.

Differences in BCT by BONE TYPE (middle section of FIG. 23) approachedsignificance (p=0.09), with the processing of VBs associated withshorter BCTs (6.32 hours) compared to IL (8.51 hours). This occurredbecause Facility B, which processed only VBs, had shorter BCTs thanFacility A. Outcomes also differed by BONE TYPE. Compared to IL, VBsyielded higher numbers of CFU-TOTAL (341.29 vs. 97.44 per 10⁵ cells,respectively; p=0.02) and CFU-GM (50.46 vs. 18.03 per 10⁵ cells,respectively; p=0.04). BONE TYPE was controlled in final regressionmodels.

In cases where the body was refrigerated prior to bone recovery (rightmost section of FIG. 23), mean WITs tended to be significantly shorter(2.65 hours with, vs. 3.98 hours without, body cooling, p=0.04). Thesame was true for CITs (19.51 hours with, vs. 28.83 hours without, bodycooling, p=0.009). It is noteworthy that all outcomes were worse whenbody cooling was employed. Mean % CD34+ viability was 72.75% with, vs.89.86% without, body cooling (p=0.0001). Similarly, with and withoutbody cooling the average CFU-TOTAL count was 100.16 vs. 659.00 per 10⁵TNC plated, respectively (p=<0.00001), and the average CFU-GM count was18.52 vs. 94.85 per 10⁵ TNC plated, respectively (p<0.00001). BODYCOOLING was accounted for in both initial and final ischemia-timeregression models.

Ischemia-Time Regression Models

Unadjusted (base) regression models used only WIT, BCT, and CIT aspredictors (no adjustments for other covariates). These models aresummarized in the below Technical Appendix C, FIG. 27 to FIG. 29. SinceBONE TYPE, FACILITY and EXPERIENCE were found to be significantvariables associated with outcomes (FIG. 23), adjusted models weredeveloped to control statistically for the influence of thesecovariates.

The beta regression model predicting % CD34+ viability is shown in FIG.24. (Details of beta regression are provided in the below TechnicalAppendix A). The percentage of viable CD34+ cells that were recovered,declined significantly as a function of increasing BCT, with the declineoccurring at a diminishing rate as % CD34+ approached zero (lineareffect, p=0.002; second-order polynomial effect, p=0.03). A similarcurvilinear decline in % CD34+occurred in relation to increasing CIT(linear effect, p=0.003; second-order polynomial effect, p=0.005).Neither BONE TYPE nor WIT were significant. EXPERIENCE (p=0.09) and theFACILITY×EXPERIENCE interaction (p=0.07) approached statisticalsignificance. Odds ratios measure the change in % CD34+associated with aone-unit change in the associated predictor variable. For example, theodds ratio associated with a one-hour increase in WIT is 0.9663,indicating that each one-hour increase in WIT reduces % CD34+ to 96.63%of its previous value. The model's predictive validity is evidenced bythe similarity of the estimated parameters of the original model (leftpanel of FIG. 24) to those of the bootstrap models (right panel). Themodel is statistically significant (p=0.001).

Results of the linear regression of CFU-TOTAL is shown in FIG. 25. Herethe importance of BONE TYPE as a source of BM cells is revealed. When BMcells were recovered from VB rather than IL, CFU-TOTAL increased by207/10⁵ TNC plated (p=0.025). The effect of BCT on CFU-TOTAL wasnegative. As BCT increased, the recovery of CFU-TOTAL decreased, withthe decline occurring at a diminishing rate (linear effect, p=0.00005;second-order polynomial effect, p=0.002). The effects of WIT and CITwere not statistically significant. EXPERIENCE also was not significant,however, EXPERIENCE was retained because model performance improved whenEXPERIENCE was controlled statistically. When BONE TYPE and EXPERIENCEwere both controlled statistically, the model's explanatory powerimproved from R²=35% to 47%. The adjusted R² also improved from 35% to40%, indicating that the improvement was not the result of modelover-specification. Model precision also improved, as indicated bysmaller standard errors. The similarity of the estimated parameters ofthe original model (left panel of FIG. 25) to the averaged results ofthe bootstrap models (right panel) is evidence of the model's predictivevalidity. The model was significant (p=0.000005) and explained 47.3% ofthe variation in CFU-TOTAL.

Results of the linear regression of CFU-GM are shown in FIG. 26. Thebest CFU-GM model included BONE TYPE, but not EXPERIENCE or FACILITY, ascontrol variables. Although BONE TYPE was not statistically significant,it was retained in the model because its inclusion improved theexplanatory power from R²=32% to 34%, while the adjusted R² remained thesame (29%), suggesting the model is not over-specified. With BONE TYPEcontrolled statistically, WIT and BCT continue to demonstratestatistically significant associations with CFU-GM. Each passing hour ofWIT reduces CFU-GM by −7.19/10⁵ TNC plated (p=0.03), while each hour ofBCT reduces CFU-GM by −5.24/10⁵ TNC plated (p=0.00003). CIT had noeffect (p=0.86). The model's predictive validity is evidenced by thesimilarity in the parameters of the original model (left panel of FIG.26) to the averaged results of the bootstrap models (right panel). Themodel is significant (p<0.00001) and explains just under 34% of thevariability in CFU-GM.

Predictions from % CD34+Model

A range of predictions generated from the adjusted beta regression modelof FIG. 24 are shown in FIG. 15. The pattern of predictions illustratehow various combinations of WIT and CIT alter the viability of recoveredCD34+ cells. The predictions in FIG. 15 are the outcomes expected whenbody cooling is not employed. Calculated values in each square representthe percentage of viable CD34+ cells recovered from whole BM. Thegradient of shading demonstrates the overall interrelationship betweenWIT and CIT. The dense shading extending from the top left until thelightly-shaded region or until the unshaded region and comprisingnumbers greater than or equal to 80% represents values above 80%viability; the dense shading extending from bottom right towards theunshaded region, including the lightly-shaded region and unshadedregion, and comprising numbers below or equal to 79% represents valuesbelow the 80% viability threshold. Input values used in the betaregression model to calculate CD34+ viability predictions were asfollows: BCT=0 hours (no body cooling); Facility B=0 (Indianapolis);Experience=12 (mean for Indianapolis); Bone Type VB=0. WIT and CITvalues are varied from the 10^(th) to 90^(th) percentile of observedvalues

An inspection of the range of WIT and CIT values reveals that WIT ismore detrimental to cell viability than CIT. When WIT is held to 3 hoursor less, the viability of CD34+ cells remains at or above 80% (greenregion) for up to 24 hours of CIT. However, as WIT is extended beyond 3hours the amount of CIT that can be tolerated is progressivelyshortened. We did not test the effect of cryopreservation, thereforethese predicted values do not account for possible loss of viability dueto subsequent freezing and thawing of recovered cells. Similarpredictions for CFU-TOTAL and CFU-GM can be made using the coefficientsprovided in FIG. 25 and FIG. 26.

Discussion

In terms of total numbers of donors and number of bones procured, thispragmatic observational field study is the largest to date and the firstto quantify the influences of ischemia and body-cooling times on thequality of HSPCs recovered from the bones of deceased donors. The studywas designed with the intent of producing externally valid data that canbe generalized and applied in routine practice settings. The studyencompassed the full continuum of ischemia times likely to be seen undernormal OPO operating conditions, and differed from previous studiesconducted at single institutions where donor bones were recoveredimmediately after cardiac arrest (i.e., no body cooling), with rapidbone recovery (i.e., short WIT), and without the need for long periodsof transport (i.e., reduced CIT) [4, 14, 15].

The study had three primary objectives: (a) to quantify the statisticalrelationships between ischemia time and the quality of HSPCs derivedfrom deceased donors, (b) to determine the boundary conditions beyondwhich longer ischemia times adversely affect the quality of HSPCs, and(c) to investigate whole-body cooling as a tactic for preserving cellfunction and viability. The study results convey four principalmessages.

First, acceptable levels of HSPC quality are achievable despite theprolonged ischemia times that are inevitable when bones must be procuredby geographically dispersed OPOs and shipped cross-country to a distantprocessing center. Our analyses show that, under such conditions,favorable combinations of warm- and cold-ischemia times are readilyachievable, enabling CD34+ cell viabilities in the range of 80-90%.Overall, the unadjusted mean percentage of viable CD34+ cells recoveredwas just under 80% (79.3%, Table 2).

The second message is that refrigerating the body prior to bonerecovery, a practice that is common in the recovery of tissues, isdetrimental to the viability and function HSPCs recovered from cadavericBM. When whole-body cooling was used, CD34+ cell viability averaged72.75%; when body cooling was not used, average viability reached nearly90% (89.96%, Table 3), suggesting that the optimal practice would be todispense with body cooling and transfer recovered bone as quickly aspossible to a cold ischemic environment.

Third, the source of BM (bone type) matters. Our analysis shows that VBsare a superior source of viable HSPCs compared to IL. In unadjustedcomparisons, CD34+ cell viabilities exceeded 80% when VB was the source,but fell below 74% when IL was the source (Table 3). The reason for thisdifference is not clear and is probably multifaceted. It is likely thatvariation in the isolation processes used with the two bone types was amore important factor than physiological differences. Given that this isthe first study to compare BM from deceased donor VBs and IL, no otherdirectly comparable data exist. The closest approximation is comparisonsof the viability of CD34+ cells recovered from the BM of deceased donorVBs and living donor aspirated iliac crest, which showed no difference[24-26].

The fourth message that our analysis conveys is that experience mattersand may vary substantially across different processing centers. As withmost technical activities, the processing of BM cells from cadavericbone follows a learning curve. It is well established that the productsof industrial manufacturing improve with learning, a phenomenon firstdocumented over 80 years ago [27] and subsequently incorporated intostandard textbooks on operations management [17, 28]. In more recenttimes it has been shown that the learning-curve phenomenon extends toboth the outcomes and cost of medical procedures [18-20]. We observeddifferent learning trajectories in the BM processing centers we studied(results provided in the below Technical Appendix B) and, although weanalyzed only two centers, our results suggest that the pace of learningand the shape of the learning curve may vary substantially acrosscenters, a factor that should be considered in the design of futuretraining programs, BM processing protocols, and certification practices.Our analysis implies that a volume-outcome relationship exists for theprocessing of BM and that a high-volume, regional center that hasaccumulated more processing experience may produce a higher-quality BMproduct compared to a low-volume center.

Although, the intent of the present study was not to optimize yield orviability, some comparisons can be made with data from previous reportsof deceased human donor BM recovery where optimization was the goal.Three previous studies have compared BM from a combined total of 99deceased donors to that of a combined 58 living donors [24-26]. In thesereports, the percentage of CD34+ cells from deceased donor BM (2.1+0.6%;mean+standard deviation) was not statistically different (p=0.32) thanBM aspirated from living donors (1.56+0.92%). This compares well withour findings in which the average percentage of CD34+ cells recoveredwas 2.43+0.64% (mean+standard error). We did observe greater variationin CD34+percentages, which likely reflects the extreme range of ischemiatimes and, consequently, the quality of donor tissue on arrival in ourstudy.

The quality of deceased-donor CD34+HSPCs also has been compared toliving-donor HSPCs by assaying CD34+ cell viability and CFU potential[24-26]. Mean viability of CD34+ cells recovered from deceased donorswas 95.2+3.6% compared to 93.5+0.35% for living donors. Functionalequivalency of deceased-donor and living-donor BM HSPCs was establishedby comparing the frequency of CFU-GM, which was 105±65 per 10⁵ TNCplated in deceased-donor BM, compared to 81.4±17 per 10⁵ TNC plated inliving donor BM. By comparison, our overall averages from deceased-donorBM (Tables 2 and 3) were lower for both of these quality metrics,presumably due, again, to the extreme range of ischemia times and theinclusion of body cooling in our study, which negatively impactedaverage cell viabilities. We have subsequently used the findingsreported here to establish limits of 8 hours WIT and 30 hours CIT, andhave eliminated the practice of body cooling. Following this protocolchange, vertebrae from 50 donors meeting these criteria have now beenrecovered and processed (manuscript in preparation). The averageCD34+HSPC viability was 90.5+1.9% and the average CFU-GM was158.3+13.5/10⁵ TNC plated, which is comparable to the previous studies[24-26].

Overall, our study demonstrates the feasibility of recovering highvolumes of BM from deceased donors for banking. Building a BM bank withsufficient HLA diversity requires an ample source and steady supply ofdeceased-donor medullary bones. Fortunately, the Uniform Anatomical GiftAct of 1968 established a syndicate of 58 geographically distributedOPOs. Each year approximately 10,000 deceased individuals donate theirorgans and a further 40,000 donate tissues, yielding approximately30,000 organs and over a million tissues recovered annually(unos.org/data/transplant-trends/accessed 29 Nov. 2019). The high volumeof bones potentially recoverable through this network, could provide thenecessary inventory to justify the establishment of an integrated systemof bone procurement, recovery, and transport, linked to BM processingand banking centers. An integrated system of this type would require thecooperation and coordination of multiple OPOs all following agreed-uponoperational protocols. Our study demonstrates the feasibility ofbuilding such a system using existing OPO infrastructure. In particular,we have demonstrated that protocols can be developed and enforced, tomaintain a favorable ischemic environment from the point of boneprocurement and recovery, through cross-country shipping, to arrival atthe BM processing center. Because our data were unconstrained for thevariables of body-cooling and ischemia times, they likely possess a highlevel of external validity (generalizability), and the results of ourpredictive models (FIG. 2) can be used to establish realisticischemia-time tolerance limits and HSPC quality acceptance criteria.

The creation of a BM banking system would offer several distinctadvantages over current living-donor registries. First, personal risk tolive donors would be obviated, as opposed to only ameliorated throughthe present predominant practice of mobilized peripheral bloodcollection. Second, much larger volumes of HSPCs can be recovered from adeceased donor compared to a living donor, allowing for multipleinfusions from the same donor in cases of graft failure. Additionally,recovered cells can be packaged in known quantities, tested for quality,and cryopreserved for later on-demand use. Because the units arecryopreserved, they can be stored indefinitely [29], thereby obviatingthe problem of attrition that occurs with living-donor registries.Finally, a BM bank can serve as a readily available resource duringsurges in demand following a mass casualty event, such as a nucleardisaster resulting in widespread bone-marrow failure [1, 2].

For these advantages to be fully realized, a host of logistic andsystems issues will have to be addressed. Chief among these is therecognition that the prolonged ischemia times introduced during bonerecovery, shipment and processing, need to be effectively managed toassure the quality and yield of BM products. BM banking is now cominginto existence and is beginning to display significant potential. Fromthe perspective of a nascent BM processing facility, the results of ourstatistical models can be used to establish quantitative ischemiatolerance limits and quality acceptance standards for safeguarding theviability and function of HSPCs derived from cadaveric bone. From abroader policy perspective, our models can also provide the foundationfor an emerging BM banking system to institute data-driven industrystandards.

REFERENCES

-   1. Knebel, A. R., et al., Allocation of scarce resources after a    nuclear detonation: setting the context. Disaster Med Public Health    Prep, 2011. 5 Suppl 1: p. S20-31.-   2. Weinstock, D. M., et al., Radiologic and nuclear events:    contingency planning for hematologists/oncologists. Blood, 2008.    111(12): p. 5440-5.-   3. Kawai, T., et al., Long-term results in recipients of combined    HLA-mismatched kidney and bone marrow transplantation without    maintenance immunosuppression. Am J Transplant, 2014. 14(7): p.    1599-611.-   4. Schneeberger, S., et al., Upper-extremity transplantation using a    cell-based protocol to minimize immunosuppression. Ann Surg, 2013.    257(2): p. 345-51.-   5. Spitzer, T. R., et al., Twenty Year Follow Up of    Histocompatibility Leukocyte Antigen-Matched Kidney and Bone Marrow    Co-Transplantation for Multiple Myeloma with End Stage Renal    Disease: Lessons Learned. Transplantation, 2019.-   6. Hotta, K., et al., Long-term Nonhuman Primate Renal Allograft    Survival Without Ongoing Immunosuppression in Recipients of Delayed    Donor Bone Marrow Transplantation. Transplantation, 2018. 102(4): p.    e128-e136.-   7. Yamada, Y., et al., Overcoming memory T-cell responses for    induction of delayed tolerance in nonhuman primates. Am J    Transplant, 2012. 12(2): p. 330-40.-   8. Eckardt, J. R., et al., Comparison of engraftment and acute GVHD    in patients undergoing cryopreserved or fresh allogeneic BMT. Bone    Marrow Transplant, 1993. 11(2): p. 125-31.-   9. Lioznov, M., et al., Transportation and cryopreservation may    impair haematopoietic stem cell function and engraftment of    allogeneic PBSCs, but not BM. Bone Marrow Transplant, 2008.    42(2): p. 121-8.-   10. Stockschlader, M., et al., Use of cryopreserved bone marrow in    allogeneic bone marrow transplantation. Bone Marrow    Transplant, 1995. 15(4): p. 569-72.-   11. Stockschlader, M., et al., Use of cryopreserved bone marrow in    unrelated allogeneic transplantation. Bone Marrow Transplant, 1996.    17(2): p. 197-9.-   12. AATB, Guidance Document, in Evaluation of Body Cooling at    Standard D5.400. 2013, American Association of Tissue Banks: McLean,    Va. p. 13.-   13. Schwartz, D. and J. Lellouch, Explanatory and pragmatic    attitudes in therapeutical trials. J Chronic Dis, 1967. 20(8): p.    637-48.-   14. Donnenberg, A. D., et al., Clinical implementation of a    procedure to prepare bone marrow cells from cadaveric vertebral    bodies. Regen Med, 2011. 6(6): p. 701-6.-   15. Gorantla, V. S., et al., Development and validation of a    procedure to isolate viable bone marrow cells from the vertebrae of    cadaveric organ donors for composite organ grafting.    Cytotherapy, 2012. 14(1): p. 104-13.-   16. Sutherland, D. R., et al., The ISHAGE guidelines for CD34+ cell    determination by flow cytometry. International Society of    Hematotherapy and Graft Engineering. J Hematother, 1996. 5(3): p.    21326.-   17. Riahi-Belkaoui, A., The learning curve: a management accounting    tool. 1986, Westport, Conn.: Quorum Books. xiii, 245 pages.-   18. Flood, A. B., W.R. Scott, and W. Ewy, Does practice make    perfect? Part 1: The relations betweeen hospital volume and outcomes    for selected diagnostic categories. Medical Care, 1984. 22(2): p.    98-114.-   19. Flood, A. B., W. R. Scott, and W. Ewy, Does practice make    perfect? Part II: The relation between volumes and other hospital    characteristics. Medical Care, 1984. 22(2): p. 115-125.-   20. Woods, J. R., et al., The learning curve and the cost of heart    transplantation. Health Serv Res, 1992. 27(2): p. 219-38.-   21. Harrel Jr, F. E., Regression modeling strategies with    applications to linear models, logistic regression, and survival    analysis. 2nd ed. Springer Series in Statistics. 2001, New York:    Springer. 582.-   22. Ferrari, S. L. P. and F. Cribari-Neto, Beta regression for    modeling rates and proportions. J. Applied Statistics, 2004.    31(7): p. 799-815.-   23. Picard, R. and D. Cook, Cross-validation of regression    models. J. Am. Stat. Assoc, 1984. 79(428): p. 1303-1313.-   24. Ahrens, N., et al., Mesenchymal stem cell content of human    vertebral bone marrow. Transplantation, 2004. 78(6): p. 925-9.-   25. Rybka, W. B., et al., Hematopoietic progenitor cell content of    vertebral body marrow used for combined solid organ and bone marrow    transplantation. Transplantation, 1995. 59(6): p. 871-4.-   26. Soderdahl, G., et al., Cadaveric bone marrow and spleen cells    for transplantation. Bone Marrow Transplant, 1998. 21(1): p. 79-84.-   27. Wright, T., Factors affecting the cost of airplanes. J    Aeronautical Sciences, 1936. 3(2): p. 122128.-   28. Green, J. H., Operations Management: Productivity and Profit.    1984: Reston Pub Co. 723.-   29. Woods, E. J., et al., Off the shelf cellular therapeutics:    Factors to consider during cryopreservation and storage of human    cells for clinical use. Cytotherapy, 2016. 18(6): p. 697-711.    Technical Appendix A—Experience Models

To account for learning we created a variable, EXPERIENCE, defined asthe number of donors processed prior to the current donor. Donors werenumbered serially from i=1 . . . n, in the order they were processed,and EXPERIENCE was coded i−1, to denote the fact that EXPERIENCE wasalways one less than the current donor being processed. Facility A beganprocessing bone marrow five months before Facility B, and becauseFacility B had the advantage of participating in (and learning from)cases processed at Facility A, the two facilities had different learningtrajectories. To account for this difference, EXPERIENCE was codedseparately for each facility. To identify the two facilities in themodel, we coded FACILITY A=1 and FACILITY B=0.

Regression Model:

Outcomes (% CD34+, CFU-TOTAL/10⁵, and GM-TOTAL/10⁵) were modeled aslinear combinations of FACILITY (where the processing occurred),EXPERIENCE (number of cases processed at the facility prior to thecurrent case), and the FACILITY×EXPERIENCE interaction. % CD34+wasmodeled using beta regression (see Technical Appendix B). The other twooutcomes (CFU-TOTAL and CFU-GM) were modeled using traditional OLSlinear regression. Models had the following linear form:Y=β ₀+β₁(FACILITY)+β₂(EXPERIENCE)+β₃(FACILITY×EXPERIENCE)  [A,1]

-   -   Where: =Outcome (% CD34+, CFU/10⁵, or GM/10⁵)        -   β₀=intercept (constant term)        -   β₁=Coefficient associated with FACILITY        -   β₂ Coefficient associated with EXPERIENCE        -   β₃=Coefficient associated with the FACILITY×EXPERIENCE            interaction

The interaction term, β3, accounts for the possibility that Facility Amay have had a different linear relationship with EXPERIENCE (adifferent learning trajectory) than Facility B.

Algebra for Deriving Testable Effects

The model for Facility A is:

$\begin{matrix}\begin{matrix}{Y = \begin{matrix}{\beta_{0} + {\beta_{1}\left( {{FACILITY}\mspace{14mu} A} \right)} + {\beta_{2}({EXPERIENCE})} +} \\{\beta_{3}\left( {{FACILITY}\mspace{14mu} A \times {EXPERIENCE}} \right)}\end{matrix}} \\{= {\beta_{0} + {\beta_{1}(1)} + {\beta_{2}({EXPERIENCE})} + {\beta_{3}\left( {1 \times {EXPERIENCE}} \right)}}} \\{= {\left( {\beta_{0} + \beta_{1}} \right) + {\left( {\beta_{2} + \beta_{3}} \right) \times ({EXPERIENCE})}}}\end{matrix} & \left\lbrack {A{.2}} \right\rbrack\end{matrix}$

The model for Facility B is reduced because Facility B=0 and the termsassociated with β1 and β3 drop out of the model. Thus the model forFacility B is:

$\begin{matrix}\begin{matrix}{Y = \begin{matrix}{\beta_{0} + {\beta_{1}\left( {{FACILITY}\mspace{14mu} B} \right)} + {\beta_{2}({EXPERIENCE})} +} \\{\beta_{3}\left( {{FACILITY}\mspace{14mu} B \times {EXPERIENCE}} \right)}\end{matrix}} \\{= {\beta_{0} + {\beta_{1}(0)} + {\beta_{2}({EXPERIENCE})} + {\beta_{3}\left( {0 \times {EXPERIENCE}} \right)}}} \\{= {\beta_{0} + {\beta_{1}(0)} + {\beta_{2}({EXPERIENCE})} + {\beta_{3}(0)}}} \\{= {\left( \beta_{0} \right) + {\left( \beta_{2} \right) \times ({EXPERIENCE})}}}\end{matrix} & \left\lbrack {A{.3}} \right\rbrack\end{matrix}$

The difference between Equations [A.2] and [A.3] provides insights intothe effects that are testable in the model:

$\begin{matrix}\begin{matrix}{{FACILITY}\mspace{14mu}{A:}} & {Y = {\left( {{\beta 0} + {\beta 1}} \right) + {\left( {{\beta 2} + {\beta 3}} \right) \times ({EXPERIENCE})}}} \\{{- {FACILITY}}\mspace{14mu}{B:}} & {Y = {({\beta 0}) + {({\beta 2}) \times ({EXPERIENCE})}}} \\{{{FACILITY}\mspace{14mu} A} - B} & {({\beta 1}) + {\left( \beta_{3} \right) \times ({EXPERIENCE})}}\end{matrix} & \left\lbrack {A{.4}} \right\rbrack\end{matrix}$

From [A.4], the following effects are testable as null hypotheses:

-   H0₁:β₀=0 Tests the significance of FACILITY B's intercept relative    to an intercept of zero. (The outcome for Facility B when    EXPERIENCE=0).-   H0₂:β₁=0 Tests the significance of FACILITY A's intercept relative    to FACILITY B's intercept. (The outcome for FACILITY A relative to    FACILITY B when EXPERIENCE=0).-   H0₃:β₂=0 Tests the significance of the learning slope far Facility B    (the change in FACILITY B's outcome associated with each additional    learning EXPERIENCE).-   HO₄:β₃=0 Tests the significance of the learning slope for FACILITY A    relative to FACILITY B's slope.

Example Calculation

Following is a worked example for predicting the effect of EXPERIENCE onCFU-GM/10⁵ given the regression coefficients and observed data values inTable S2:

TABLE S2 Regression coefficients and ischemia time values LinearRegression Coefficients Predictor Variable Observed Data 13₀ = 111.91Constant 13₁ = −99.34 FACILITY A = 1; B = 0 13₂ = −3.57 EXPERIENCE 20Previous Cases 13₃ = 4.17 FACILITY × EXPERIENCE A = (1 × 20); B = (0 ×20)

Assume that both FACILITY A and FACILITY B have processed 20 previousdonors, what would their respective outcomes be for the 21^(st) donor?

[A.5] Expected Outcome for FACILITY B:

$\begin{matrix}{{{CFU} - {{GM}/10^{5}}} = {\left( \beta_{0} \right) + {\left( \beta_{2} \right) \times ({EXPERIENCE})}}} \\{= {111.91 + {\left( {- 3.57} \right) \times (20)}}} \\{= 40.51}\end{matrix}$Interpretation for FACILITY B:

Starting with no experience (EXPERIENCE=0), the beginning CFU-GM yield(intercept term) is expected to be 130=111.91 CFU-GM/10⁵. Eachadditional donor processed is then expected to subtract 132=−3.57CFU-GM/10⁵ from FACILITY B's beginning amount. For the 21st donorprocessed, FACILITY B's expected yield would be(130)+(132×20)=111.91+(−3.57×20)=40.51 CFU-GM/10⁵.

[A.6] Expected Outcome for FACILITY A:

$\begin{matrix}{= \begin{matrix}{{{CFU} - {{GM}/10^{5}}} = {{\left( {{\beta 0} + {\beta 1}} \right) \times ({FACILITY})} +}} \\{\left( {{\beta 2} + {\beta 3}} \right) \times \left( {{FACILITY} \times {EXPERIENCE}} \right)}\end{matrix}} \\{= {{\left( {111.91 - 99.34} \right) \times (1)} + {\left( {{- 3.57} + 4.17} \right) \times \left( {1 \times 20} \right)}}} \\{= {(12.57) + \left( {0.60 \times 20} \right)}} \\{= {(12.57) + (12.00)}} \\{= 24.62}\end{matrix}$Interpretation for FACILITY A:

With no experience, FACILITY A's yield is estimated to beβ0+β1=[111.91+(−99.34)]=12.57 CFU-GM/10⁵ units more than FACILITY B's.Each additional case that FACILITY A processes addsβ2+β3=(−3.57+4.17)=0.60 CFU-GM/10⁵ to FACILITY A's starting yield. Forthe 21st case processed, FACILITY A's yield would be(β0+β1)+(β2+β3)×(20)=12.57+(0.60×20)=24.57 CFU-GM/10⁵. Note thatFacility A's learning slope is positive, adding β3=4.17 CFU-GM/10⁵ witheach additional case processed, while Facility B's learning slope isnegative, subtracting β2=−3.57 CFU-GM/10⁵ with each case. This isillustrative of a classic interaction. Expressed in relative terms, eachadditional learning experience is associated with a net gain ofβ2+β3=(−3.57+4.17)=0.60 CFU-GM/10⁵ for FACILITY A relative to FacilityB.

These examples illustrate the pattern that emerged for all threeoutcomes. FACILITY A, which began processing BM cells before FACILITY B,started at a relatively lower performance level and improvedmonotonically with each additional case processed. By comparison,FACILITY B, having participated in (and learned from) FACILITY A'sinitial work, started at a higher level of performance but did notchange significantly or declined slightly with increasing experience.

Technical Appendix B—Beta Regression

In the main text, we use CD34+ to denote the count of recovered CD34+cells and we use % CD34+ to denote the percentage of total CD34+ cellsthat were viable. That is:% CD34+=(Viable CD34+)/[(Viable CD34+)+(Nonviable CD34+)]

Because it is a ratio, % CD34+ is confined to the closed unit interval(0≤% CD34+≤1), meaning that it can assume values of 0% or 100% or anyvalue in between, but it cannot be less than 0% or greater than 100%.Given this restriction, ordinary least squares (OLS) linear regressionproduced unrealistic fitted values that exceeded the intervalboundaries—some of the predicted values were less than 0% and someexceeded 100%. To correct for this, we considered beta regression [1]instead of OLS linear regression for models of % CD34+. Maximumlikelihood beta regression is used to model beta-distributed randomvariables, which makes it particularly useful in situations such as ourswhere the response variable is a rate or proportion measured on acontinuous scale and bounded by minimum and maximum values. We continuedto use OLS linear regression to model the other two outcome variables,CFU-TOTAL and CFU-GM.

Here we use pCD34=% CD34+ to denote the percent of recovered CD34+ cellsthat were viable. To ensure that the outcome was evaluable as abeta-distributed variable, we transformed pCD34 as follows:pCD34*=[1+100(pCD34)]/102

This transformation restricts pCD34* to the open interval, (0<pCD34*<1),thereby satisfying the distributional assumption that the outcomevariable can approach, but cannot equal 0% or 100%. The restrictedproportion, pCD34* was then modeled by beta regression. For ease ofinterpretation, predicted values from beta regressions were backtransformed to obtain:Pred(pCD34)=[(102(Pred(pCD34*))−1)/100]=Pred(pCD34)=Pred(% CD34+).Beta Regression Equation:

The beta regression equation utilizes the logit link function of theoutcome to η, a linear predictor. Our basic beta regression equation forpredicting pCD34* was:η=ln[pCD34⁺/(1−pCD34⁺)]=β₀+β₁(WIT)+β₂(BCT)+β₃(BCT²)+β₃(BCT²)β₄(CIT)+β₅(CIT²)  [B.1]

Where:

-   -   β₀=Constant (intercept)    -   β₁=Coefficient associated with warm ischemic time (WIT)    -   β₂=Coefficient associated with body cooling time (BCT)    -   β₃=Coefficient associated with body cooling time squared (BCT²)    -   β₄=efficient associated with cold ischemia time (CIT)    -   β₆=Coefficient associated with cold ischemia time squared (CIT²)

Example Calculation

To illustrate the calculations, we use the coefficients and ischemiatimes shown in Table S3.

TABLE S3 Regression coefficients and ischemia time values BetaRegression Observed Data Coefficients Predictor Variable (IschemiaTimes) 13₀ = 3.500 Constant 13₁ = −0.01996 Warm Ischemia Time (WIT) 1.92WIT hours 13₂ = −0.181 Body-Cooling Time (BCT) 0.00 BCT hours 13₃ =0.007 Body Cooling Time squared 0.00 BCT hours² (BCT)² 13₄ = −0.111 ColdIschemia Time (CIT) 14.92 CIT hours 13₅ = 0.002 Cold Ischemia Timesquared 222.606 hours² (CIT)²

Using Equation [B.1] we solve for as shown in Equation [B.2], below

$\begin{matrix}\begin{matrix}{\eta = \begin{matrix}{{{In}\left\lbrack {{pCD}\;{34^{*}/\left( {1 - {{pCD}\; 34^{*}}} \right)}} \right\rbrack} + \beta_{0} + {\beta_{1}({WIT})} + {\beta_{2}({BCT})} +} \\{{\beta_{3}\left( {BCT}^{2} \right)} + {\beta_{4}({CIT})} + {\beta_{5}\left( {CIT}^{2} \right)}}\end{matrix}} \\{= \begin{matrix}{3.500 + {\left( {- 0.01996} \right)(1.92)} + {\left( {- 0.181} \right)(0)} +} \\{{(0.007)(0)} + {\left( {- 0.111} \right)(14.92)} + {(0.002)(222.606)}}\end{matrix}} \\{= 2.2507688}\end{matrix} & \left\lbrack {B{.2}} \right\rbrack\end{matrix}$

Because they are related to the outcome variable through a nonlinearfunction, the coefficients of the linear predictor, η, lack a simpleintuitive meaning. However, by applying the inverse link function to ηwe obtain a result that is easier to interpret.

The Inverse Link Function:

The inverse link function, exp(i)[1+erp (i)], converts the linearpredictor, η, to the expected

value of the outcome variable pCD34*:

${E\left\lbrack {{pCD}\; 34^{*}} \right\rbrack} = {\frac{\exp(\eta)}{\left\lbrack {1 + {\exp(\eta)}} \right\rbrack} = \frac{\exp\left\lbrack {\beta_{0} + {\beta_{1}({WIT})} + {\beta_{2}({BCT})} + {\beta_{3}\left( {BCT}^{2} \right)} + {\beta_{4}({CIT})} + {\beta_{5}\left( {CIT}^{2} \right)}} \right\rbrack}{1 + {\exp\left\lbrack {\beta_{0} + {\beta_{1}({CIT})} + {\beta_{2}({BCT})} + {\beta_{3}\left( {BCT}^{2} \right)} + {\beta_{4}({CIT})} + {\beta_{5}\left( {CIT}^{2} \right)}} \right.}}}$

Applying the inverse link function to the predicted value, η=2.2507688,calculated from Equation [B.2], we obtain the expected value, E[pCD34*]:E[pCD34⁺]=exp(2.2507688)/[1+exp(2.2507688]≈0.905≈90.5%  [B.3]

This result is interpretable as the expected value of pCD34* for thespecified values of the predictors given in Table S3.

To interpret the result of [B.3] in terms of pCD34 (the percentage ofviable CD34+ cells), we use the back transformation:

$\begin{matrix}{{{pCD}\; 3\; 4^{*}} = {\left. \frac{1 + {100{pCD}\; 34}}{102}\rightarrow{{pCD}\; 34} \right. = {\frac{{102{pCD}\; 34^{*}} - 1}{100} = {\frac{\left\lbrack {{(102)(0.905)} - 1} \right\rbrack}{100} = 0.9131}}}} & \left\lbrack {B{.4}} \right\rbrack\end{matrix}$

Equation [B.4] says that for the values specified in Table S3, theexpected percentage of viable CD34+ cells is pCD34=% CD34+=91.31%.

Determining the Expected Impact on pCD34* of a One-Unit Change in aGiven Predictor Variable:

The beta regression coefficient for any given predictor can be used toestimate the impact on pCD34* of a one-unit change in that predictor,controlling for all other predictors in the equation. This isaccomplished via exponentiation of the particular regression coefficientunder consideration. For example, to calculate the impact of a one-hourincrease in warm ischemia time (WIT) on the ratio of the percent ofviable CD34+ cells to the percent nonviable CD34+ cells, the ratio underconsideration is:

$\frac{{pCD}\; 34^{*}}{\left( {1 - {{pCD}\; 3\; 4^{*}}} \right)} = {{\frac{\exp(\eta)}{1 + {\exp(\eta)}}/\left\lbrack {1 - \frac{\exp(\eta)}{1 + {\exp(\eta)}}} \right\rbrack}{\exp(\eta)}}$

From Table S3, the regression coefficient associated with WIT isβ1=−0.01996. Therefore, the impact of a one-hour increase in WIT on theratio of percent viable to percent nonviable CD34+ cells is:

$\begin{matrix}{\frac{\exp\left\lbrack {\beta_{0} + {\beta_{1}\left( {{WIT} + 1} \right)} + {\beta_{2}({WCT})} + {\beta_{3}({CIT})} + {\beta_{4}\left( {CIT}^{2} \right)}} \right\rbrack}{\exp\left\lbrack {\beta_{0} + {\beta_{1}({WIT})} + {\beta_{2}({WCT})} + {\beta_{3}({CIT})} + {\beta_{4}\left( {CIT}^{2} \right)}} \right\rbrack} = {{\exp\left( {- 0.01996} \right)} = 0.98}} & \left\lbrack {B{.5}} \right\rbrack\end{matrix}$

Equation [B.5] says that if WIT were increased by one hour, while theother variables in the equation (BCT and CIT) remained unchanged, theratio of the percent viable to percent nonviable CD34+ cells woulddecrease by a factor of 2%, to 98% of its previous value. We previouslycalculated pCD34*=0.905, in Equation [B.3]. Thus, a one-hour increase inWIT would reduce pCD34* to 0.98×0.903=0.885, a 2% reduction.Equivalently, this would reduce the predicted pCD34 from 0.913 to 0.893,approximately a 2% reduction. The multiplicative factor, 0.98, is aconstant applicable to any unit change along the continuous range ofwarm ischemia times. Factors for the other predictors can be obtained inthe same way to estimate the impact of a unit change in BCT and CIT onpCD34*.

Technical Appendix C—Unadjusted (Base) Ischemia-Time Regression Models

In initial (base) regression models we used only WIT, BCT, and CIT aspredictors (no adjustments for other covariates). Results of thesemodels for % CD34+, CFU-TOTAL, and CFU-GM are shown in Tables S4-S6.Model results are shown in the left panels of the tables; averagedresults of 200 cross-validated models (each estimated with oneobservation omitted from the full dataset) are shown in the rightpanels.

Beta regression models for % CD34+ are shown in FIG. 27. Therelationship of WIT to % CD34+was not statistically significant,however, BCT (linear component, p=0.001 and second-order polynomialcomponent, p=0.01), and CIT (linear component, p=0.001 and second-orderpolynomial component, p=0.004) were both curvilinearly related to %CD34+. In both cases the CD34+yield declines in response to increasingBCT and CIT, but then slightly increases at the upper extremes of BCTand CIT. The odds ratios in FIG. 27 are continuous-variable ratios ofviable CD34+ cells to total CD34+ cells and depict the impact of aone-unit change in a given predictor. These quantities are obtained viaexponentiation of the regression coefficient associated with theparticular predictor under consideration. For example, the coefficientassociated with WIT is β1=−0.01996. (FIG. 27) Exponentiation producesthe following result:eβ=e−0.01996=0.9802 or 98.02%,

which is the value of the continuous odds ratio for Warm Ischemia shownin FIG. 27. This result says that with BCT and CIT held constant, eachone unit (one hour) increase in WIT reduces the ratio of viable tononviable CD34+ to 98.02% of its previous value. The multiplicativefactor, 0.9802, is a constant applicable to a one-unit change anywherealong the continuous range of WIT. Factors for the other predictors areprovided in FIG. 27 and can be used to estimate the effect of a one-unitchange in BCT or CIT holding other variables in the equation constant.The beta-regression prediction equation is statistically significant(p=0.0009).

The right half of FIG. 27 shows the averaged results of bootstrappedcross-validations, which provide estimates of the original model'svalidity in predicting future observations [2]. If the original modelwas misspecified the parameters of the re-estimated bootstrapped modelswould differ from the parameters of the original model. However, asrevealed in FIG. 27, model parameters (regression coefficients, standarderrors, and 95% confidence intervals) associated with the original model(left panel) are nearly the same as the corresponding parametersgenerated through bootstrap re-sampling (right panel), providingevidence of the original model's predictive validity when applied tofuture data drawn from the same population. [Technical details regardingbeta-regression and example calculations are provided in TechnicalAppendix B, above].

FIG. 28 shows linear regression results for CFU-TOTAL. The coefficientsin linear regressions are direct estimates of the impact of a unitchange in the associated predictor. BCT is the only statisticallysignificant predictor in FIG. 28. WIT and CIT are not significant. Therelationship of BCT to CFU-TOTAL is curvilinear, indicating that eachone-hour increase in BCT decreases CFU-TOTAL by −95.03639/10⁵ cells(p<0.0001) while simultaneously increasing CFU-TOTAL by 3.45603/10⁵(p=0.0008). Together, the linear and second-order polynomial componentscombine to produce a decreasing trend in CFU-TOTAL that decays at adecelerating rate. The model parameters (left panel of FIG. 28) aresimilar to the averaged parameters of the bootstrapped models (rightpanel), providing evidence of the original model's predictive validity.The model is statistically significant (p=0.00002) and explains 35% ofthe variance in CFU-TOTAL.

FIG. 29 shows linear regression results for CFU-GM. In this model theinfluences of WIT and BCT are statistically significant, while theinfluence of CIT is not significant. With CIT and BCT held constant,each hour of WIT reduces CFU-GM by −8.11295/10⁵ (p=0.01). With WIT andCIT constant, each hour of BCT reduces CFU-GM by −5.52927/10⁵(p<0.000009). The right side of FIG. 29 shows that the estimatedparameters of the bootstrapped models are similar to those of theoriginal model, again providing evidence of the original model'spredictive validity when applied to future data. The model isstatistically significant (p=0.00002) and accounts for 32% of the totalvariation in CFU-GM counts.

REFERENCES

-   1. Ferrari S L P, Cribari-Neto F. Beta regression for modeling rates    and proportions. Journal of Applied Statistics. 2004, 31(7):799-815-   2. Harrel Jr, F. E., Regression modeling strategies with    applications to linear models, logistic regression, and survival    analysis. 2nd ed. Springer Series in Statistics. 2001, New York:    Springer. 582

What is claimed is:
 1. A method for recovery of transplantable stemcells from cadaver bone or cadaver bone fragments, the methodcomprising: (a) obtaining cadaver bone or cadaver bone fragments,optionally, processing the cadaver bone into cadaver bone fragments; (b)combining the cadaver bone fragments with a grinding medium comprising anuclease that cleaves both DNA and RNA and one or more of human serumalbumin (HSA), heparin, an electrolyte medium, and a growth media,thereby obtaining grinding medium-treated cadaver bone fragments; (c)processing the grinding medium-treated cadaver bone fragments to extractbone marrow cells; and (d) collecting the extracted bone marrow cells,thereby recovering the transplantable stem cells.
 2. The method of claim1, wherein the nuclease is present in the grinding medium at aconcentration of about 3 U/mL.
 3. The method of claim 1, wherein theelectrolyte medium comprises a sterile, nonpyrogenic, isotonic solution.4. The method of claim 1, wherein the electrolyte medium comprises a pHof about 7.4.
 5. The method of claim 1, wherein the grinding mediumcomprises two or more of HSA, heparin, the electrolyte medium, and thegrowth media.
 6. The method of claim 5, wherein the grinding mediumcomprises three or more of HSA, heparin, the electrolyte medium, and thegrowth media.
 7. The method of claim 1, wherein the growth media isIscove's Modified Dulbecco's Media (IMDM).
 8. The method of claim 1,wherein the processing in step (c) comprises grinding the cadaver bonefragments to obtain ground cadaver bone.
 9. The method of claim 8,wherein the processing in step (c) further comprises a filtering stepthereby producing a filtered product comprising extracted bone marrowcells.
 10. The method of claim 9, wherein the filtering comprises a No.40 (425 μm) sieve and/or a No. 80 (177 μm) sieve.
 11. The method ofclaim 10, further comprising combining the filtered product withadditional grinding medium having components of step (b), therebyobtaining a diluted bone marrow cell product.
 12. The method of claim11, further comprising a step of further filtering the diluted bonemarrow cell product with one or more filters comprising pore sizesranging from about 200 μm to about 825 μm and/or selected from filtershaving pore sizes of 200 μm, 500 μm, or 825 μm.
 13. The method of claimof claim 12, further comprising a step removing fat from an intermediateproduct.
 14. The method of claim 1, wherein the cadaver bone fragmentsare present in or divided into 1.5 cm² pieces in step (a).
 15. Themethod of claim 1, wherein the cadaver bone or the cadaver bonefragments are derived from a vertebral body, an ileum, or a combinationthereof.
 16. The method of claim 15, wherein the cadaver bone or cadaverbone fragments are derived from a plurality of cadaver bones from thesame donor.
 17. The method of claim 1, wherein the extracted stem cellscomprise hematopoietic stem cells.
 18. The method of claim 1, whereinthe extracted stem cells comprise a Mesenchymal stem cells (MSCs). 19.The method of claim 6, wherein the grinding medium comprises theelectrolyte medium, 10 U/mL heparin, 2.5% HSA, and 3 U/mL of thenuclease.
 20. The method of claim 1, wherein the transplantable stemcells are suitable for transplantation into a human subject.